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Best Neuroscience PhD Programs: Careers, and More [2024]

Are you looking for the best neuroscience PhD programs of 2024? You’re lucky because I have compiled the best neuroscience PhD programs list. Before we get into the individual programs, let’s first dive into what neuroscience is.

Neuroscience is a branch of biological science studying the brain, emphasizing its biochemistry, molecular biology, psychology, and anatomy to understand human and animal behavior. It offers an in-depth understanding of brain diseases and abnormalities so we can develop solutions using studies with neuroscientific models.

An expert neuroscientist can make significant contributions to society, and a PhD in neuroscience will equip you to pursue a prestigious career in the field. According to Salary Expert , the average annual salary of neuroscience PhD holders is $113,946. That number is expected to rise to $129,991 by 2028, making this one of the highest-paying PhDs .

Ready to find your dream PhD program in neuroscience? Let’s get started.

Table of Contents

Best Neuroscience PhD Programs

Harvard university, harvard medical school.

Ph.D. Program in Neuroscience (PiN)

The Neurobiology Department of Harvard Medical School is the first research department in the world to take an interdisciplinary, systemic approach to studying the human brain. This program is one of the more competitive PhDs in neuroscience and offers a wide range of electives in a flexible format. Students can easily balance their coursework and lab work with hybrid and online learning.

• Courses : Quantitative methods for biologists, rotations in neuroscience, and discipline of neuroscience.
• Duration : 3 years or more
• Delivery : On-campus
• Tuition : Full funding
• Financial aid : Full tuition/stipend support, health insurance, childcare support, parental support, and travel allowance.
• Acceptance rate:  5%
• Location : Boston, Massachusetts

Massachusetts Institute of Technology

Brain and Cognitive Sciences PhD Program

MIT’s Department of Brain and Cognitive Sciences claims to produce the world’s sharpest and most innovative brain scientists. This PhD program enables students to pursue cutting-edge research that seeks to push the boundaries of neuroscientific knowledge.

• Courses : Molecular & cellular neuroscience, computational cognitive science, and statistics for neuroscience research.
• Duration : 5 years plus
• Tuition : $29,875 per term
• Financial aid:  Scholarships, loans, and health insurance.
• Acceptance rate : 7.3%
• Location : Cambridge, Massachusetts

Stanford University, School of Medicine

Neurosciences Ph.D. Program

Stanford is one of the leading research universities in the world. This PhD program is one of 14 “Biosciences Home Programs” offered by the institution’s School of Medicine. One of the best neuroscience PhD programs the USA provides, it enables students to design their post-graduate studies by working collaboratively with an extensive network of faculty and labs.

• Courses : Responsible conduct of neuroscience, neuroscience systems core, and neurogenetics core.
• Credits : 135 units
• Duration : 5 years
• Tuition : Refer tuition page
• Financial aid: Fellowships, grants, research assistantships, teaching assistantships, and veteran benefits.
• Acceptance rate : 5.2%
• Location : Stanford, California

Princeton University, Graduate School

Ph.D. in Neuroscience

Princeton University is a globally acclaimed school with a long list of Nobel laureates and other honors. This one in our list of the best neuroscience PhD programs emphasizes hands-on experience, encouraging students to apply the concepts they learn in lectures in the lab.

• Courses : Cellular & circuits Neuroscience, computational neuroscience, and Statistics for Neuroscience.
• Tuition : $59,710 per year
• Financial aid : Fellowships, research assistantships, teaching assistantships, external funding, travel grants, veteran benefits, and loans.
• Acceptance rate : 5.6%
• Location : Princeton, New Jersey

Yale University, School of Medicine

Interdepartmental Neuroscience Program

Yale is another world-renowned university with several cultural centers to preserve the institution’s unique cultural identity. This interdepartmental PhD program is called a “department without walls” as it allows students to explore every aspect of neuroscience with the help of over 100 faculty members from more than twenty departments.

• Courses : Principles of neuroscience, foundations of systems neuroscience, and bioethics in neuroscience.
• Duration : Up to 7 years
• Tuition : $48,300 per year
• Financial aid : Fellowships, awards, research assistantships, loans, and travel funds.
• Acceptance rate : 6.5%
• Location : New Haven, Connecticut

The University of California San Francisco, Weill Institute for Neurosciences

Neuroscience Graduate Program

The University of California San Francisco is a big name committed to diversity and follows the JEDI (justice, equity, diversity, and inclusion) approach to promote a positive campus environment. This post-graduate program allows students to work collaboratively with faculty members across various departments who are well-known names in their respective fields.

• Courses : Cellular & molecular neuroscience, systems & behavioral neuroscience, and computational neuroscience.
• Duration : 4 – 6 years
• Tuition : $11,442 per year
• Financial aid : Fellowships, awards, grants, and teaching assistantships.
• Acceptance rate : 3.7%
• Location : San Francisco, California

Brown University

Brown University is located in the culturally diverse city of Providence, Rhode Island. The program emphasizes intellectual freedom and has an “Open Curriculum” system at the undergraduate level, which confirms this. This PhD in neuroscience program involves various experimental approaches, including a Graduate Partnership Program (GPP) with NIH (National Institutes of Health).

• Courses : Advanced molecular & cellular neurobiology, advanced systems neuroscience, and neuroanatomy.
• Tuition : $8,207 per course
• Financial aid : Full funding, stipend, health insurance, grants, fellowships, and teaching assistantships.
• Acceptance rate : 7.7%
• Location : Providence, Rhode Island

Johns Hopkins University, School of Medicine

Neuroscience Training Program

The Neuroscience Department at Johns Hopkins University was one of the country’s first academic centers for Neuroscience. Its PhD program is well-regarded, offering students ample opportunities for lab rotations, a wide selection of electives, and seminar series from eminent national and international scholars.

• Courses : Neuroscience cognition, quantitative methods for the brain sciences, and neuron models.
• Duration : 3 years plus
• Tuition : Full tuition, stipend, and benefits
• Financial aid:  Fellowships, loans, scholarships, and grants.
• Acceptance rate : 11.1%
• Location : Baltimore, Maryland

California Institute of Technology, Division of Biology and Biological Engineering

Neurobiology Graduate Program

Caltech is a private institution dedicated to excellence in technological education and research. This Ph.D. program allows students to conduct advanced research in molecular mechanisms of nervous system development, the evolution of the brain and behavior in primates, neuroscience of brain disorders, and neuro-engineering.

• Courses : Tools of neurobiology, molecular, cellular, and developmental neurobiology, and circuits, systems, and behavioral biology.
• Credits : 54 units (6 quarter courses)
• Tuition : $56,364 per year
• Financial aid : Teaching assistantships, fellowships, loans, research assistantships, and full funding.
• Acceptance rate : 6.7%
• Location : Pasadena, California

The University of Chicago, Biological Sciences Division

PhD Program in Computational Neuroscience

The University of Chicago is a renowned institution that has pioneered neuroscience research by eminent scientists like K. C. Cole, Stephen Polyak, and Jack Cowan. The school’s PhD in Computational Neuroscience offers an in-depth understanding of how various neural components affect human and animal behavior.

• Courses : Cellular neurobiology, methods in computational neuroscience, and behavioral neuroscience.
• Tuition : $19,035 per quarter
• Financial aid : Grants, fellowships, awards, stipends, and research assistantships.
• Location : Chicago, Illinois

What Do I Need to Get a PhD in Neuroscience?

You’ll need an undergraduate degree in biological sciences or a related field. Some programs may also require a master’s in a relevant field; others may ask for GRE scores as part of the application process. You must complete coursework, research, and a dissertation paper throughout the program, meet teaching requirements and seminars, and pass qualifying examinations.

What to Consider When Choosing a Neuroscience PhD Program

Neuroscience is a highly specialized field that often involves interdisciplinary research. Therefore, looking for programs offering specializations in your areas of interest and with faculty members who are experts in these fields is essential. It’s also vital to consider applicable tuition, other fees, location, and whether the program offers the type of study you want (on-campus, online, or hybrid learning).

Once you decide on the best neuroscience PhD program for you, laying some groundwork is a good idea. This will help you create a more robust application and better prepare for the program. Read up on the latest neuroscience research and think about potential subjects for your dissertation. Build your sector network and start making connections that will help you with your studies and beyond.

Why Get a Doctorate in Neuroscience?

A doctorate in neuroscience can make you a valuable expert in one of the top branches of the biological sciences. You’ll have plenty of opportunities in this field to perform exciting, valuable, and innovative research.

This advanced degree will also qualify you for many well-paid roles, including:

• Medical Science Liaison ( $149,911 )
• Senior Clinical Research Associate ( $114,764 )
• Neuroscientist ( $81,661 )
• Research Scientist ( $87,532 )
• Program Director, Healthcare ( $87,780 )
• Assistant Professor, Postsecondary/Higher Education ( $73,907 )

PhD in Neuroscience: Key Facts

What is the average cost of a phd in neuroscience.

The cost of completing a Ph.D. in neuroscience varies depending on factors like the school, the program, and other expenses like accommodation. A reputable PhD in neuroscience program can range anywhere from $10K to $60K per year.

How Long Does It Take to Get a PhD in Neuroscience?

Getting a PhD in Neuroscience usually takes between 3 and 7 years.

What Skills Do You Gain from a PhD in Neuroscience?

A PhD in Neuroscience awards you a range of skills, most notably:

• The ability to develop testable neuroscientific hypotheses  and conduct studies using experimental, statistical, and literature review methods.
• Laboratory skills  related to  researching behavioral Neuroscience concepts.
• Scientific written communication skills.

PhD Neuroscience Program Statistics

• A PhD in neuroscience program can expect hundreds of applicants — the average is around 170 .
• Most neuroscience PhD candidates have an undergraduate degree in psychology, biology, or neuroscience , though they may have backgrounds in other fields, even non-science ones such as business or humanities.
• Most schools only accept a few neuroscience PhD candidates a year based on stringent criteria. For example, The University of Texas at Dallas accepts an average of 10-20 students per year.

Key Takeaways

With intake numbers for PhDs in neuroscience programs being relatively small, it’s essential to start preparing early to assemble the most robust application possible. Once you get accepted into your dream program, the future will be bright, with the Bureau of Labor Statistics estimating a 10% growth in jobs for medical scientists between 2022 and 2032. From high salary prospects to the opportunity to make valuable contributions to society, you’re sure to have a rewarding career as a neuroscientist!

If you’re deciding between neuroscience and psychology, check out our guides to the best Master’s in Psychology  and the best online PhD in Psychology programs .

Frequently Asked Questions

How competitive are neuroscience doctoral programs.

Neuroscience PhD programs can be highly competitive. Even when there are hundreds of applicants, only 10 or so may be accepted each year by each program. Therefore, it’s essential to have a strong academic record and prepare a compelling application to be accepted into your dream program.

Do Neuroscientists Need a PhD?

This depends on the exact neuroscience role you want. Typically, you’ll need a PhD in neuroscience to work as a research scientist, senior research associate, or neuroscience professor at a post-secondary school. However, you may be eligible for entry-level neuroscience roles with an undergraduate or master’s degree .

Does Harvard Have a Neuroscience Major?

Yes, Harvard University offers one of the USA’s most reputable neuroscience doctorate programs .

Lisa Marlin

Lisa is a full-time writer specializing in career advice, further education, and personal development. She works from all over the world, and when not writing you'll find her hiking, practicing yoga, or enjoying a glass of Malbec.

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Neuroscience Institute

Ph.d in neural computation.

Computational neuroscience is an area of brain science that uses technology to develop and analyze large data sets that are used to understand the complexities of neurobiological systems. In recent years, these methods have become more and more vital to the field of neuroscience as a whole. The use of quantitative methods in neurophysiology has led to important advances, and there has been a continuing stream of related work within mathematics and applied physics. More recently, engineers, computer scientists, and statisticians have contributed to the field, further expanding the definition of computational neuroscience.  At the same time, the number of investigators with requisite skills who are ac­tively engaged in this domain of research is relatively small. There is a widely recognized need for increased training in the application of computational, mathematical, and sta­tistical methods to biology and medicine, and to problems in neuroscience in particular.

The Ph.D. Program in Neural Computation seeks to train new scientists in the field. The environment at Carnegie Mellon University and the University of Pittsburgh has much to offer to students interested in computational approaches and it is a perfect home for the Ph.D. Program in Neural Computation. The neuroscience community in Pittsburgh is known for being particularly strong in computation.  The program also offers joint Ph.D. degrees with  Machine Learning  and  Statistics .

This program is designed to attract students with strong quantitative backgrounds and to train them in quantitative disciplines relevant to neuroscience and also to provide them the essential background in experimental neuroscience.  

In doing so, we leverage the special strengths of our institution and the unique neuroscience community here in Pittsburgh. Training faculty and courses will be drawn both from CMU and Pitt as described. The PNC PhD program is designed for stu­dents with backgrounds in computer science, physics, statistics, mathematics, and engineering who are interested in computational neuroscience, particularly with an emphasis on quantitative methods from computer science, machine learning, statistics and nonlinear dynamics.

The program consists of the following core activities:

• Coursework in computational neuroscience, quantitative methodologies and experimental neuroscience
• Research milestone presentations
• Exposure to experimental approaches through rotations or thesis research
• Training in teaching, scientific presentations and responsible conduct of research
• Successful defense of a PhD Thesis

Additional satellite activities through the CNBC will also foster students’ professional and scientific development.   Read more about the curriculum .

The PNC program is overseen by the PNC training faculty, the Academic Program Manager, and the Program Directors.  Questions about any aspect of the program should be directed either to the Academic Program Manager,   Melissa Stupka , or one of the Program Directors:   Steve Chase at CMU and   Gelsy  Torres-Oviedo  at University of Pittsburgh.

Joint Programs

• PNC/ Machine Learning
• PNC/ Statistics
• M.D.-Ph.D. Program

The CMU Rales Fellow Program is dedicated to developing a diverse community of STEM leaders from underrepresented and under-resourced backgrounds by eliminating cost as a barrier to education. Learn more about this program for master's and Ph.D. students. Learn more

Diversity in Neuroscience

• CMU Diversity, Equity, and Inclusion
• Dietrich College Diversity and Inclusion
• Mellon College of Science Diversity
• CMU Rales Fellows Program

Neural Computation Contacts

Academic program manager.

Melissa Stupka Mellon Institute 116C [email protected]

Program Director (CMU)

Steve Chase Professor, Biomedical Engineering & Neuroscience Institute Carnegie Mellon University Mellon Institute 115N [email protected]

Program Director (Pitt)

Gelsy Torres-Oviedo, Ph.D.                   Associate Professor, Bioengineering University of Pittsburgh Schenley Place, Room 115 [email protected]  

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Emory University Laney Graduate School Neuroscience Graduate Program

Home » Overview » Computational Neuroscience

• Computational Neuroscience

Seeking a degree in Computational Neuroscience?

The graduate program in Neuroscience at Emory University is an interdisciplinary program, spanning many departments and priding itself on a collaborative atmosphere encouraging excellence. Our faculty and students have a broad scope of research interests within neuroscience, ranging from molecular to cellular to behavioral neuroscience.

Our program is one of eight Ph.D. programs that comprise the Emory Graduate Division of Biological and Biomedical Sciences (GDBBS). There are over 260 faculty members in the Division, and graduate students of any program in the Division face no departmental barriers. They can do laboratory rotations and research with any of the Division Faculty. The can also switch to one of the other programs, have an advisor from another program and take any course offered by the Division. This structure gives students tremendous flexibility in choosing coursework, advisors and research plans. There are currently 98 students enrolled in the program, and the average time to finish the degree is about 5.5 years.

Joint Degree Programs

• Neuroscience/Biomedical Engineering In 1997, Emory University and the Georgia Institute of Technology joined forces to create a joint Biomedical Engineering Department , which includes 15 Emory faculty members (4 from Neuroscience), and over 25 faculty members from Georgia Tech. The collaboration provides enormous opportunity and intellectual resources for students interested in neuroengineering, neuronal modeling, computational neuroscience and other cutting-edge challenges. Emory has eleven faculty that participate in this joint effort. The Chairman of the new department, Dr. Don Giddens, headed the Johns Hopkins College of Engineering before coming to Emory.
• MD/PhD Approximately 10-15% of Neuroscience graduate students are working towards an M.D. degree or already have one. MD/PhD students are admitted through the MD/PhD Program .

Financial Aid

In addition, all students must participate in weekly seminars for the first 2 years of the program. These seminars are informal venues where students present either relevant papers or their own research to their peers and a small group of faculty members. From this experience, students learn presentation and communication skills essential to a career in science.

Related Terms

• Behavioral Neuroscience
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• Molecular Neuroscience
• Neurodegeneration
• Neuropharmacology Research
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PhDs in Neuroscience and Computational Neuroscience

For contact information, please visit the Graduate Program for Neuroscience website .

Program Description

The Graduate Program for Neuroscience (GPN) is a University-wide PhD degree-granting training program in neuroscience that unites the graduate training faculty and students present on our two campuses, the Charles River Campus (CRC) and the Medical Campus (MED). We are a diverse community of faculty, students, and staff who come from multiple departments, schools and colleges, and campuses of the University. Our individual disciplinary interests combine to form a comprehensive research and educational environment that rejects all forms of racism and thrives on our shared excitement for neuroscience.

Faculty administration of the program is delivered by the Program Director in association with the GPN Steering Committee, Graduate Education Committee, and the Computational Neuroscience Curriculum Committee. The research of GPN training faculty covers virtually all areas of neuroscience, from molecular and cellular to systems and computational.

In addition to the PhD in Neuroscience, there is a specialized PhD in Computational Neuroscience for students interested in a more rigorous curriculum in the area. Students pursuing the PhD in Computational Neuroscience get a strong primary training in neuroscience that is shared with their fellow students pursuing the PhD in Neuroscience through the “Core” Curriculum.

An essential feature of the GPN training mission for all students (PhD in Neuroscience and PhD in Computational Neuroscience) is a set of core courses that are aimed at developing a community of thinkers, who move through their training together, building relationships that cross interdepartmental and intercampus barriers, and foster cross-disciplinary collaborations. Most students complete their PhD in roughly 5.8 years.

As members of the unified program, the Neuroscience faculty serve as thesis research mentors and/or knowledge facilitators and work together to help students close any gaps between their knowledge base of individual disciplines as well as their understanding of computational and experimental models. Every effort is expended to provide an individually tailored mentorship and educational program for each student that builds upon their unique strengths and interests, while also recognizing areas that need enrichment through faculty guidance and curriculum choice.

There are four aspects of modern neuroscience that our program addresses:

• First , it is becoming increasingly clear that important breakthroughs in the field require ideas, approaches, and techniques originating from many disciplines. The GPN curriculum provides both a broad cross-disciplinary core education including molecular, cellular, and systems; cognitive and behavioral; computational; and clinical neuroscience; and the flexibility to take neuroscience-related coursework in any of the departments and programs of the University to build depth of specialization along different perspectives in a particular area of neuroscience.
• Second, a critical aspect of GPN is the formation of a unified group of graduate students from across BU, including the Colleges of Arts & Sciences, Engineering, Health & Rehabilitation Sciences: Sargent College, and BU’s medical school. For the first year of training in GPN, these students take the “core” curriculum courses together, have the opportunity to be involved in common projects, and participate as a community in all Boston University neuroscience activities.
• Third , critical to the interdisciplinary focus of the training, is the participation of traditional science departments, which provide a large number of the elective courses and specialized training opportunities to complement the GPN curriculum. Several departments at the Medical Campus (Anatomy & Neurobiology and Pharmacology, Physiology & Biophysics) also offer a joint degree in neuroscience that is coordinated with GPN to further enhance the interdisciplinary nature of the student community.
• Fourth , a strong emphasis is placed on building relationships among students and faculty across multiple disciplines to complement the traditional mentorship by the thesis advisor and to provide entry into the neuroscience research/student community of multiple BU schools with alternative scientific perspectives.

The Diverse Student Body

Because students who enter GPN come from diverse backgrounds—including psychology, engineering, biology, chemistry, physics, and mathematics—upon their mutual acceptance into the program, they will be given the opportunity to fill any gaps in their training that might interfere with their ability to do their best in the upcoming core curriculum of their first and second years. This could mean enrolling in a particular summer course(s); taking a summer hands-on laboratory methods section (Tools of the Trade) organized by GPN faculty to introduce basic techniques in molecular or behavioral research; or even structured readings/discussions over the summer with a faculty member that are designed to stimulate a deeper understanding of a core discipline such as biology, biochemistry, or mathematics that might not have been fully emphasized in undergraduate coursework.

It is our belief that with a coherent educational program that embraces multiple complementary attitudes and approaches to scientific inquiry—breadth vs. depth, multidisciplinary vs. traditional discipline, basic vs. clinical science, and experimental approaches vs. theoretical (computational)—there is the greatest opportunity to create a young generation of researchers with sufficient expertise and flexibility to be able to come together and address some of the “big problems” in neuroscience.

Learning Outcomes

By taking a common set of core courses, students will develop an advanced understanding in the diverse field of neuroscience from molecular to cellular and systems to human cognition. The Computational Neuroscience curriculum supplements core neuroscience training with advanced training in a wide array of computational methods for (i) studying the nervous system; (ii) developing neuroscience-related technologies; (iii) and the critical thinking to use this knowledge to conduct rigorous and reproducible scientific discoveries. Students will also develop an appreciation for the human condition by participating in clinical rotations held at the West Roxbury VA Hospital and BMC where they have the opportunity to follow cases in neurology, neurosurgery, and psychiatry.

Experimental Proficiency

Students will develop scientific proficiencies that will permit them to undertake graduate-level research in their area of interest. Students are required to participate in laboratory rotations in their first year to aid in the development of important scientific skills in lab bench or computational research as well as identifying a laboratory for their future thesis research. During their second year, students enroll in electives that enhance their research interests. All GPN students must take the mandated workshop requirement in Responsible Conduct of Research (RCR) that is offered across the University with BU faculty participation.

Analytical Skill Development

Students will develop quantitative skills that permit the analysis of data in their field of research. First-year students begin their analytical skill development with a seven-week intensive introductory course in Computational Neuroscience using Python that emphasizes data analysis and mathematical modeling for students regardless of where they are in their use of quantitative and programming skills. Subsequently, students further develop these skills by enrollment in a course in probability and statistics that is relevant to their research. Mentors work with students to help them to develop the quantitative and analytical skills particular to their research areas.

Communication

Students will be able to communicate their research and that of the work of others orally and in written formats. This proficiency is developed via their curriculum in Frontiers in Neuroscience and their written qualifying exam that is in the form of an NRSA fellowship application. After students pass their qualifying exam, they are required to present seminars to the larger graduate neuroscience community annually. Roughly one year prior to their defense, students present a longer progress report seminar to the full neuroscience community in a formal setting. Students regularly present posters and papers for campus events as well as at national conferences such as SfN.

As the Graduate Program for Neuroscience is a cross-University program, significant emphasis is placed on building relationships that cross inter-departmental and inter-campus barriers and foster cross-disciplinary collaborations. Students are expected to show good citizenship by volunteering in ways that support the local community through outreach activities such as bringing science activities to the local public schools, mentoring activities of fellow students, and cultural enrichment for our own community through events like the Neural Arts Forum. We also encourage participation in our student groups (NGSO and CNSO) that help to sustain the student community via social and educational interactions.

Curriculum Overview

Most students take 28 credits of required study that includes laboratory rotations and clinical rounds, as described below, and fulfill the 64-credit post-bachelor’s, or the 32-credit post-master’s, requirement for the PhD by participating in the student seminar series, attending GPN-sponsored activities such as the distinguished lecture series and the neuroscience retreat, and from directed study with their thesis research mentor and GPN faculty facilitators.

In the first year, students take 18 credits of core coursework (courses taken together as an entering class) that cover the diverse field of neuroscience, from molecular to cellular and systems to human cognition (12 credits), an introductory course in computational modeling that is tuned to the specific background of individual students (2 credits), and Frontiers in Neuroscience (4 credits) where they share lunch every week with a member of the broad group of faculty that make up the neuroscience community here at BU; develop important oral presentation skills; and learn to critically evaluate the literature in their field of interest as well as in areas outside of their earlier academic and research training. Here they also develop important writing skills through faculty and peer mentoring and acquire the basic skills to write a compelling Specific Aims section of an individual training grant application.

During the first year, students also receive required credit (2–4 credits) for participating in laboratory rotations that help them develop important skills in lab bench research as well as identify a laboratory for future thesis research.

Second Year

During their second year, students choose elective curriculum (12 credits) that enhances their research interests (some of our elective curriculum is organized into discipline-specific training opportunities that enable our students to receive T32 support, see below), develop an appreciation for the human condition by participating in a unique opportunity to observe clinical cases in neurology, neurosurgery, and psychiatry (1–2 credits), and take an elective in probability and statistics that is relevant to their research. Together throughout their time in GPN they also take the mandated workshop requirement in Responsible Conduct of Research (RCR) that is offered across the University with BU faculty participation and have access to multiple GMS and faculty-organized workshops in grant writing and professional development. GMS is especially proud of its accomplishments in being able to deliver an exceptional professional development curriculum, having received the BEST award from NIH in 2018.

Computational Neuroscience

For those students wanting to specialize in computational neuroscience , there is additional required study that leads to the Doctor of Philosophy (PhD) in Computational Neuroscience. Computational neuroscience students take their first-year “core” classes with all GPN students and a minimum of two (rather than three) laboratory rotations, with at least one that gives them the experience of experimental research. Additional rotations can be arranged if a student wants to do more and this is encouraged by GPN leadership.

Additional Curriculum

All students have the option of taking additional academic coursework rather than using directed study credits with the thesis mentor to make up the 64-credit requirement for the degree, especially as needed based upon their research interests or to supplement a lack of certain background during undergraduate study.

The goal for the majority of students will be to complete core requirements and to choose the laboratory for their thesis research by the end of the first year. Course requirements for elective study will most likely be completed by the end of the second year. All efforts will be made to tailor the training program to the individual goals of the student, taking into account their previous training experiences either at the undergraduate or master’s level. GPN committees will continually evaluate, expand, and redesign core coursework and choices of advanced electives in order to offer students the best curriculum available across the University.

Core Courses

An essential feature of the program is a set of “core” courses: these are taken by all students in GPN (Neuroscience & Computational Neuroscience) during their first year and are aimed at developing a community of thinkers who move through the training program together, building relationships that cross departmental and campus barriers, and foster cross-disciplinary collaborations.

Students complete 12 credits of “core” neuroscience coursework that provides a strong foundation in this diverse field of graduate study. The fall semester course Systems Neuroscience I (4 credits) is a team-taught lecture/discussion course that meets on alternate days on the Charles River Campus and the Medical Campus. The curriculum engages students to develop basic skills in critical thinking as well as basic principles of brain function, neuroanatomy, and the cellular and molecular neurobiology that will be essential for them as they move into the spring semester integrated curriculum (8 credits) that critically evaluates the use of novel technologies, model vertebrate/invertebrate systems, computational models, and studies with human subjects, with the goal of providing the most up-to-date thinking that can elaborate on the function and dysfunction of the human brain.

• GMS NE 700 Principles of Neuroscience I: From Molecules to Systems (4 cr)
• GRS NE 741 Neural Systems I: Functional Circuit Analysis (4 cr)
• GRS NE 742 Neural Systems II: Cognition and Behavior (4 cr)

Additional “core” neuroscience requirements include: a seven-week intensive introductory course in data analysis and mathematical models for students regardless of where they are in their use of quantitative and modeling skills. This introductory course combines lectures and hands-on computer time to treat real laboratory data like case studies and motivates students to use the mathematical approach as a means to better understand their own research via statistical data analysis and modeling.

• GRS MA 665 An Introduction to Mathematical Models and Data Analysis in Neuroscience (2 cr)

Students pursuing the PhD in Computational Neuroscience (or who have taken an undergraduate course in the area) can substitute a more advanced elective for this requirement. Likewise, students who have taken the required course and would like more exposure to the area can continue on in the class to take the next module that is offered sequentially (4 credits instead of 2 credits).

Additional Required Curriculum

In addition to the core curriculum, students take the following seminar coursework during their first year and enroll in laboratory rotations:

• GRS NE 500/501 Frontiers in Neuroscience (4 cr)

During the first semester, students attend a unique weekly journal club and professional development class that is run by the GPN Director over lunch on Fridays (fall and spring of Year 1). In the fall semester, students are assigned key papers from a BU faculty member’s laboratory and supporting manuscripts in the field. The particular faculty member, whose research is being reviewed, cohosts the class with the Director. During the two-hour session, student presenters review and critique experimental findings and approaches, building their skills in critical thinking and developing the basic tools for successful oral presentations. They also get to share their scientific ideas and interests with the leaders of neuroscience at Boston University, an activity that enriches our neuroscience community. Research from monthly GPN distinguished lecturers from across the world are integrated into the training experience to provide a balanced exposure for students to all areas of neuroscience and to give them firsthand interactions with exceptional individuals who are defining the field of the future.

In the spring semester, students learn to write a compelling Specific Aims and Approach section for an individual training grant application and develop the peer group skills to help each other grow professionally in both oral and writing exercises. These new skills they will bring to their NRSA and/or NSF application for future research funding in Year 2 and for their written qualifying exam. The course stresses the evolution of critical thinking and the use of constructive criticism to improve the training of their fellow graduate students.

Laboratory Rotations

• GMS NE 800/801 Laboratory Rotations (2–4 cr)

Providing an enriching set of laboratory research experiences directed by GPN faculty for students during their first year is a central feature of the neuroscience training program at Boston University. The multitude of highly talented mentors who have funded research projects provides students with a large number of potential laboratories from which to choose the thesis research mentor who will complement their current interests and, through laboratory rotations, expand their horizons into different areas of investigation that they may grow toward in the future. The majority of students pursuing the PhD in Neuroscience take a minimum of three rotations, with at least one rotation in an area outside of their initial research interests; students pursuing the PhD in Computational Neuroscience take a minimum of two rotations, with at least one in an experimental laboratory. Students can also request additional rotations should they not find a mentor, or if they would like more exposure to other methodologies used in neuroscience.

Clinical Rounds (2 credits)

During their second year, all students participate in a unique opportunity to interact with human patients suffering from neuropsychiatric disorders. These experiences take place at the Boston VA supervised by a clinician scientist who is a member of the GPN training faculty.

Hands-On Laboratory Boot Camp/Neuroscience Retreat

Before starting in the training program, the Graduate Education Committee (GEC) reviews the research experiences of each student to determine whether they have had basic training in molecular, behavioral, and/or cognitive research. Based upon their history of undergraduate or post-baccalaureate experiences, they will be advised to take a series of group method sessions called Tools of the Trade, run by faculty in the summer, that provides students with the essential hands-on experience necessary to make their laboratory rotations in the fall meaningful for their graduate-level training. In Tools of the Trade, students learn some of the basic techniques necessary for conducting laboratory research in the field of neuroscience, independent of their current research interests. Students who have already had experience in both molecular and cognitive research can petition to the GEC to waive the requirement and students who are unable to attend during the summer can take the sessions as part of their Laboratory Research Experience class during the fall semester, before beginning laboratory rotations.

For instance, group activities may be organized around detection of an important neuronal RNA via real-time PCR, the identification of a single nucleotide polymorphism in a DNA sample from a patient with a neurodegenerative disease, identification of protein in brain slices using immunohistochemistry and fluorescence microscopy, electrophysiological measurements or calcium imaging of living neurons, interaction of transcription factors with DNA regulatory elements that control expression of neural-specific genes, neuroimaging of the brain to detect the activation of particular brain structures, and running of a behavioral task with animals to address questions of learning and memory. Projects vary with the expertise and interests of the participating GPN faculty.

The entering class in GPN is also invited to the annual GPN Neuroscience Retreat. Every effort is made to schedule the retreat right after the Tools of the Trade so that students are well integrated in our community before arriving in the fall for formal admittance.

Elective Study

The rest of the didactic credits toward the PhD come from elective study (12-credit minimum) that is organized for simplicity into three distinct pathways of emphasis (Molecular & Cellular, Systems, and Computational) to help students choose a relevant curriculum for their interests and choose electives within any area of neuroscience. Those students enrolled in the computational neuroscience training specialization should reference the requirements specific to that curriculum as it applies to required and elective choices. Taking advantage of the translational research and history of clinical training at the MED campus, and rehabilitative Health Sciences at the CRC, students are required to take elective coursework (minimum of 2 credits) and participate in clinical rounds (see above) that provide an exposure to patients and topics relevant to human disease (such as Autism, Alzheimer’s, Drug Abuse, Epilepsy, Parkinson’s, Schizophrenia, and Disorders of Vision, Hearing & Speech). They also take a required elective in probability and statistics that is appropriate to their area of thesis research upon the recommendation of their thesis mentor.

Program Requirements for Continued Student Registration

All students must maintain full-time enrollment each semester. Additional program credits come from Directed Study (GRS NE 901/902) during thesis research with the mentor, and attendance is required at neuroscience ethics and responsible conduct of research (RCR) workshops, at the majority of distinguished lectures, faculty seminars, and program events of the GPN (including student recruitment, the annual Neuroscience Retreat, GPN social gatherings such as the Fall Welcome Reception, the Laboratory Matching Ceremony, and the Holiday Party), and most importantly at GPN graduate student seminars. All students are required to give at least one short presentation annually at the Neuroscience Graduate Student Seminar Series and to fulfill the calendar deadlines of their graduate milestones. Please note that at least one published first author manuscript is required for moving toward the thesis defense.

As members of GPN, students will acquire their more advanced training from coursework offered in departments around the University in order to fulfill the credit requirements for the PhD degree. The following is a list of potential electives organized by topic area as a guide to help students choose their curriculum.

* Medical Campus

Relevant to Molecular, Cellular & Systems (see also Computational)

• CAS BI 520 Sensory Neurobiology (4)
• CAS BI 545 Neurobiology of Motivated Behavior (4)
• CAS BI 575 Techniques in Cellular and Molecular Neuroscience (4)
• CAS BI 599 Neurobiology of Synapses (4)
• CAS PS 530 Neural Models of Memory Function (4)
• GMS AN 702 *Neurobiology of Learning and Memory (2)
• GMS AN 709 *Neural Development and Plasticity (2)
• GMS AN 804 *Methods in Neuroscience (4)
• GMS AN 807 *Neurobiology of the Visual System (2)
• GMS BN 798 *Functional Neuroanatomy in Neuropsychology (4)
• GMS PM 860 *Electrophysiology and Pharmacology of the Synapse (2)
• GMS PM 892 *Molecular and Neural Bases of Learning Behaviors (2)
• GRS BI 644 Neuroethology (4)
• GRS BI 645 Cellular and Molecular Neurophysiology (4)
• GRS BI 655 Developmental Neurobiology (4)
• GRS BI 681 Molecular Biology of the Neuron (4)
• SAR HS 550 Neural Systems (4)
• SAR HS 755 Readings in Neuroscience (4)

Relevant to Biomedical & Translational

• CAS BI 554 Neuroendocrinology (4)
• GMS AN 707 *Neurobiology of Aging (2)
• GMS AN 713 *Autism: Clinical and Neuroscience Perspectives (2)
• GMS AN 808 *Neuroanatomical Basis of Neurological Disorders (2)
• GMS BN 782 *Forensic Neuropsychology (4)
• GMS BN 793 *Adult Communication Disorders (4)
• GMS BN 796 *Neuropsychological Assessment I (4)
• GMS BN 797 *Neuropsychological Assessment II (4)
• GMS BN 821 *Neuroimaging Seminar (2)
• GMS BN 891 & 892 *Case Studies in Neuropsychology (three different clinical rounds, sections A1, B1, and C1) (2 credits each section)
• GMS BN 893 *Child Clinical Neuropsychology (4)
• GMS IM 690 *Imaging of Neurologic Disease (2)
• GMS PM 820 *Neuropsychopharmacology (2)
• GMS PM 840 *Neuroendocrine Pharmacology (2)
• GMS PM 850 *Biochemical Neuropharmacology (2)

Behavioral & Cognitive Neuroscience

• CAS PS 520 Research Methods in Perception and Cognition (4)
• CAS PS 525 Cognitive Science (4)
• CAS PS 528 Human Brain Mapping (4)
• CAS PS 544 Developmental Neuropsychology (4)
• CAS PS 721 General Experimental (4)
• CAS PS 734 Psychopharmacology (4)
• CAS PS 737 Memory Systems of the Brain (4)
• CAS PS 738 Techniques in Systems & Behavioral Neuroscience (4)
• CAS PS 821 Learning (4)
• CAS PS 822 Visual Perception (4)
• CAS PS 824 Cognitive Psychology (4)
• CAS PS 828 Seminar in Psycholinguistics (4)
• CAS PS 831 Seminar in Neuropsychology (4)
• CAS PS 833 Advanced Physiological Psychology (4)
• CAS PS 835 Attention (4)
• ENG BE 715 Functional Neuroimaging (4)
• GMS AN 716 *Developmental Cognitive Neuroscience (4)
• GMS BN 795 *Neuropsychology of Perception and Memory (4)
• GRS PS 829 Principles in Neuropsychology (4)

Theoretical & Computational Neuroscience

• CAS CN 500 Computational Methods in Cognitive and Neural Systems (4)
• CAS CN 510 Principles and Methods of Cognitive and Neural Modeling I (4)
• CAS CN 520 Principles and Methods of Cognitive and Neural Modeling II (4)
• CAS CN 530 Neural and Computational Models of Vision (4)
• CAS CN 540 Neural and Computational Models of Adaptive Movement and Planning Control (4)
• CAS CN 550 Neural and Computational Models of Recognition, Memory, and Attention (4)
• CAS CN 560 (colisted as BE 509) Neural and Computational Models of Speech and Hearing (4)
• CAS CN 570 Neural and Computational Models of Conditioning, Reinforcement, Motivation, and Rhythm (4)
• CAS CN 580 Introduction to Computational Neuroscience (4)
• ENG BE 509 (colisted as CN 560) Quantitative Physiology of the Auditory System (4)
• ENG BE 570 Introduction to Computational Vision (4)
• ENG BE 701 Auditory Signal Processing: Peripheral (4)
• ENG BE 702 Auditory Signal Processing: Central (4)
• ENG BE 707 Quantitative Studies of Excitable Membranes (4)
• ENG BE 710 Neural Plasticity and Perceptual Learning (4)
• GRS CN 700 Computational and Mathematical Methods in Neural Modeling (4)
• GRS CN 710 Advanced Topics in Neural Modeling: Comparative Analysis of Learning Systems (4)
• GRS CN 720 Neural and Computational Models of Planning and Temporal Structure in Behavior (4)
• GRS CN 730 Models of Visual Perception (4)
• GRS CN 740 Topics in Sensory Motor Control (4)
• GRS CN 760 Topics in Speech Perception and Recognition (4)
• GRS CN 780 Topics in Computational Neuroscience (4)
• GRS CS 640 Artificial Intelligence (4)

Coursework in related disciplines:

• CAS BI 551 Biology of Stem Cells (4)
• CAS BI 552/553 Molecular Biology (4,4)
• CAS BI 555 Techniques in Cell Biology (4)
• CAS BI 556 Membrane Biochemistry and Cell Signaling (4)
• CAS BI 721 Biochemistry (4)
• CAS MA 565 Math Models in the Life Sciences (4)
• CAS MA 573 Qualitative Theory of Ordinary Differential Equations (4)
• CAS MA 581 Probability (4)
• CAS MA 582 Mathematical Statistics (4)
• CAS MA 583 Introduction to Stochastic Processes (4)
• CAS MA 584 Multivariate Statistical Analysis (4)
• CAS MA 585 Time Series and Forecasting (4)
• CAS MA 684 Applied Multiple Regression and Multivariable Method (4)
• CAS MB 722 Advanced Biochemistry (4)
• ENG BE 515 Introduction to Medical Imaging (4)
• ENG BE 540 Bioelectrical Signals: Analysis and Interpretation (4)
• ENG BE 550 Bioelectromechanics (4)
• ENG BE 560 Biomolecular Architecture (4)
• ENG BE 561 DNA and Protein Sequence Analysis (4)
• ENG BE 700 Advanced Topics in Biomedical Engineering (4)
• ENG BE 740 Parameter Estimation and Systems Identification (4)
• ENG BE 747 Advanced Signals and Systems Analysis for Biomedical Engineering (4)
• GMS BI 776 *Gene Targeting in Transgenic Mice (2)
• GMS BI 782 *Molecular Biology (4)
• GMS BI 786 *Biochemical Mechanisms of Aging (2)
• GMS BI 789 *Physical Biochemistry (2)
• GMS BI 797 *Molecular Mechanisms of Growth and Development (2)
• GMS BL 755/756 *Biochemistry (4,4)
• GMS MI 713 *Comprehensive Immunology (4)
• GMS MM 701 *Genetics and Epidemiology of Human Disease (2)
• GMS MM 703 *Cancer Biology and Genetics (2)
• GMS MM 710 *Molecules to Molecular Therapeutics: The Translation of Molecular Observations to Clinical Implementation (4)
• GMS PM 800 *Systems Pharmacology (4)
• GMS PM 832 *Pharmacogenomics (2)
• GMS PM 843 *Pharmacologic Intervention in Inflammatory Responses (2)
• GMS PM 880 *Gene Regulation and Pharmacology (2)
• GMS PM 881 *Drug Discovery and Development (2)
• GRS BI 621/622 Biochemistry (4,4)
• GRS BI 735 Advanced Cell Biology (4)
• MET AD 893 Technology Commercialization: From Lab to Market (4)

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Students with undergraduate degrees in biology, neuroscience, cognitive science, psychology, any of the quantitative sciences, or any of the engineering disciplines are invited to apply to the Graduate Program in Computational Neuroscience.

Students who are interested in Computational Neuroscience, but who prefer earning a Ph.D. in a cognate discipline can do so by pursuing a Ph.D. through the departments of Computer Science, Mathematics, Neurobiology, Physics, Psychology, or Statistics, and taking Computational Neuroscience courses.

The University of Chicago welcomes applications from students who are members of groups underrepresented in science, including students with disabilities. For information on the BSD efforts to recruit and retain students with diverse backgrounds, please visit our office of diversity .

Prerequisites:

Computational neuroscience is inherently interdisciplinary, and most students doing graduate work in this area will have strengths in one of the relevant areas and weaknesses in others. Program requirements are designed to address background deficiencies, so students with uneven backgrounds should not hesitate to apply.

A year of college-level calculus is an absolute prerequisite. Ideally, applicants should have some collegiate-level coursework in biology (optimally including an introductory neurobiology course), an introductory psychology course, and some mathematics (such as linear algebra and elementary differential equations) beyond calculus.

Students who have not had prior exposure to linear algebra and differential equations may be asked to take appropriate courses in these areas before taking the mathematics sequence within the computational neuroscience curriculum.

Recruitment:

We would love to talk to you during one of our recruitment events , and we always have a table staffed with faculty and students at the SfN graduate fair. Or came and visit us on campus if you are in Chicago. For arrangements, please contact [email protected] .

Admission to the Ph.D. program in Computational Neuroscience is coordinated through the Division of Biological Sciences. The application system opens in early September, and completed applications need to be submitted by November 30 . You may apply to as many as four programs with one application, but please list the program of most interest to you first ; this program will be given priority in reviewing your application. 

Applicants are expected to have completed a bachelor of arts (BA), bachelor of science (BS), or equivalent degree from an accredited college or university by the time they matriculate.

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International Programmes 2023/2024

International Doctoral Programme in Computational Neuroscience Doctoral Programme in Computational Neuroscience

Technische universität berlin • berlin.

• Course details
• Costs / Funding
• Requirements / Registration
• About the university
• Charité-Universitätsmedizin Berlin (Charité)

Freie Universität Berlin (FU Berlin)

• Humboldt-Universität zu Berlin (HU Berlin)
• Bernstein Center for Computational Neuroscience Berlin (BCCN Berlin)

Courses are held in English.

15 March for the following winter semester

The international doctoral programme at the Bernstein Center for Computational Neuroscience Berlin (BCCN Berlin) is an interdisciplinary research programme. Neuroscience is one of the most intensively developing and important sciences of the 21st century. Understanding the functioning of the brain requires the collaborative efforts of neurobiologists, neuropsychologists, cognitive scientists, medical researchers, computer scientists, mathematicians, physicists and engineers.

Computational Neuroscience uses theoretical approaches from this broad range of disciplines to integrate experiment, data analysis and modelling in order to understand the brain. Furthermore, it makes a scientific language available that can be used across disciplines and levels for neurobiology, cognitive science, and information technology. Computational Neuroscience may thus help to solve long-standing research questions, contribute to better prevention and treatment strategies for neural disorders, lead to unified concepts about biological processes, advance information technologies and human-machine interactions and, last but not least, provide new insight for designing efficient strategies for teaching and learning.

Students who have completed the doctoral programme will have the ability to communicate across these diverse disciplines which will help them to make their own contribution to the fast growing field of neuroscience.

The doctoral programme Computational Neuroscience is hosted by the BCCN Berlin. Doctoral students in the field of Computational Neuroscience can apply for association with the programme. They will benefit of structured supervision through the Faculty of the BCCN Berlin and will have access to the lectures for doctoral and Master's students and to a broad range of courses teaching transferable skills.

The doctoral programme of the BCCN Berlin is one of the programmes united under the umbrella of the Einstein Center for Neuroscience Berlin , which offers a four-year PhD programme with an extended orientation phase.

Teaching takes place mostly in the building of the BCCN Berlin on the Campus Nord of Humboldt-Universität zu Berlin and at the Technische Universität Berlin.

Research for the doctoral project forms the major part of the programme, complemented by coursework.

All students participate in a seminar in which they present their individual projects to ensure interdisciplinary interaction.

Invited talks by renowned international speakers take place monthly.

A lecture series on theoretical and experimental neuroscience as well as machine learning is addressed primarily to doctoral students. Lectures are held by principal investigators of the BCCN Berlin twice a month.

Additionally, students follow individualised curricula tailored to their specific needs. These curricula can comprise courses at one of the three major universities within Berlin, related graduate programmes within Berlin, summer schools, workshops or the like.

Furthermore, students are encouraged to participate in soft skills courses.

Students are required to earn 15 ECTS (20 ECTS for the Charité PhD degree) in advanced topics related to their research subject and 10 ECTS in soft skills courses such as project writing, scientific presentation, ethical and legal issues in neuroscience, etc.

Reflecting the interdisciplinary nature of the programme, each student will be supervised by one principal thesis adviser and one co-adviser whose areas of competence cover the topic of the planned thesis research. Together with one to three additional senior scientists, the adviser and the co-adviser will form the student's doctoral committee. About three months after acceptance into the programme, each doctoral student will be required to present a written project proposal and to defend the proposal in front of his or her doctoral committee. Problems regarding the competence and scientific work of the candidate or the quality of the supervision should become obvious at this point, and measures can be taken early enough to resolve these. On an annual basis, such a meeting between the doctoral student and his or her committee will take place in order to monitor the student's progress.

• International guest lecturers
• Projects with partners in Germany and abroad

International workshops, symposia, alumni workshops

Sometimes doctoral students will be involved in the teaching or tutorial activities of their working groups.

315 EUR per semester

A minimum of around 1,100 EUR per month

The BCCN Berlin Doctoral Programme does not offer scholarships or doctoral positions. However, it is possible to receive funding from the members of the BCCN Berlin. Contact possible supervisors and check if they have funding for you before submitting your application.

The programme is part of the Einstein Center for Neurosciences Berlin (ECN) . The Einstein Center offers one-year PhD scholarships covering all areas of neuroscience including computational neuroscience. The deadline for these scholarships is December/January for the following winter semester ( https://www.ecn-berlin.de/education/phd-fellowships.html) .

Students with an interest in computational neuroscience and with a strong mathematical background are welcome to apply if they hold an MSc or equivalent degree at the start of the programme.

We strongly advise you to check whether your degree entitles you to gain a doctoral degree in Germany.

A copy of a TOEFL test or equivalent certificate of proficiency in English must be provided (non-native speakers only).

Alternatively, coursework in English either at your home university or at a university abroad is acceptable as proof of proficiency in English.

https://www.bccn-berlin.de/applications/

Finding accommodation in Berlin can be a challenge. Students of the programme have several possibilities to find accommodation. Whatever district they prefer to live in, it is advisable to find a place with access to public transport. This is very convenient in everyday life and can save a lot of time.

If students are interested in residential accommodation, the "Berliner Studentenwerk" offers a number of different options, such as single rooms, apartments, or a shared flat. Rent starts at approx. 300 EUR but these accommodations are quite sought after and thus very difficult to find. Students should expect to pay approx. 500 EUR for rent.

Students can also choose to find a room or flat privately. The coordination office and the "Studentenwerk Berlin" provide links and recommendations for finding accommodation.

Career advisory service is provided by the teaching coordination as well as supervisors, soft skill courses on career development and university career service departments.

• Welcome event
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Technische Universität Berlin

The Bernstein Center for Computational Neuroscience Berlin

The centre comprises more than 60 research groups working from single-cell level up to macroscopic level, both experimentally and theoretically. It was established in 2004 and is part of the Bernstein Network for Computational Neuroscience. It integrates research groups from Humboldt-Universität zu Berlin, Technische Universität Berlin, Charité – Universitätsmedizin Berlin, Freie Universität Berlin, Potsdam University and the Max Delbrück Center for Molecular Medicine. It is a member of the Einstein Center for Neurosciences Berlin.

Technische Universität Berlin (TU Berlin) – Paving the Way to the Future

TU Berlin is part of the Berlin University Alliance and has been selected as one of the eleven German universities awarded the status of "University of Excellence". It strives to promote the accumulation of knowledge and to facilitate technological progress by adhering to the fundamental principles of excellence and quality both in teaching and in research. Strong regional, national and international networking with partners in science and industry is an important aspect in all these endeavours.

Humboldt-Universität zu Berlin (HU Berlin) – The Unity of Research and Teaching

Humboldt's ideal of the co-existence of research and teaching has become a model for universities all over the world. Central to this model is the idea of research-oriented teaching and the transfer of knowledge from the spirit of research. Students and teachers join in an endeavour to critically examine traditional bodies of knowledge and to actively advance learning. Since 2012, HU Berlin has been the recipient of funding by the German federal and state governments for its Institutional Strategy "Bildung durch Wissenschaft. Educating Inquiring Minds: Individuality - Openness - Guidance". HU Berlin is part of the Berlin University Alliance and has been selected as one of the eleven German universities awarded the status of "University of Excellence".

Charité – Universitätsmedizin Berlin (Medical School) (Charité)

The Charité is one of the largest university hospitals in Europe. Here, 3,700 doctors and scientists heal, carry out research and teach at top international level. The Charité also has an international reputation for excellence in training. It extends over four campuses, with almost 100 clinics and institutes bundled under 17 Charité Centers. In 2010, the Charité was privileged in looking back and joyously celebrating its 300th anniversary.

The FU Berlin is part of the Berlin University Alliance and has been selected as one of the eleven German universities awarded the status of "University of Excellence". Freie Universität can thus take its place as an "international network university" in the global competition among universities. Its future development strategy is focused around three strategic centres: cluster development, international cooperation and graduate studies. Development and assessment of research projects takes place within three major focus areas - area studies, humanities and life sciences.

University location

Berlin is both the capital city of Germany and one of sixteen German federal states. Berlin is Germany's largest city, with 3.5 million inhabitants. The city spreads across 892 km² and is divided into twelve districts. Incorporated into the city area are numerous forests, parks and garden plots - a total of more than 2,500 public recreational and green spaces, making Berlin a green city.

Founded in the 13th century, Berlin has had an eventful history. Practically no other metropolis has experienced such frequent, radical change, which has transformed the face of the city. Although Berlin has seen a steady growth in its importance, dazzling epochs have alternated with darker eras. Nevertheless, the formerly divided city has succeeded in becoming a vibrant metropolis in the heart of Europe.

Berlin offers a large number of things to do in your spare time. For example, there are 52 theatres and stages, 153 museums and 279 cinemas. Several magazines detailing cultural events taking place in Berlin can help you decide what to do. Exberliner, for example, is an English-language paper for Berlin.

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The UCSD Neurosciences Graduate Program is home to over 170 faculty and 100 students. Our program is frequently ranked among the top Neuroscience PhD programs in the world! Learn more about our program here!

Are you a prospective student? Learn more about how to apply to the UCSD Neurosciences Graduate Program!

Are you a current or prospective student? Learn more about the Neurosciences Graduate Program requirements.

Are you a current or prospective student? Learn more about funding, housing, and benefits as a graduate student in the UCSD Neurosciences Graduate Program.

Course Catalog

Want to know more about the UCSD Neurosciences Graduate Program curriculum? Learn more about some of the past and current courses available to NGP students.

Interested in computational neuroscience? Learn more about the Computational Neuroscience Specialization and find resources for students.

Interested in understanding more about involvement in program affairs? Learn more about the numerous committees that students and faculty take part in.

Interested in science communication, education outreach, and diversity? Learn more about our student run organizations and how you can get involved.

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PhD Program Admissions

Visualization of copper in normal (top row) and mutant (bottom row) zebrafish brains. Image by Tong Xiao, Chang lab.

The application deadline for Fall 2024 admission was November 27th, 2023 (by 8:59 pm Pacific Standard Time). 

The Neuroscience PhD Program grants PhDs only. We do not offer a master’s degree. Applications are accepted from the middle of September through the end of November for admission for Fall of the following year. We do not accept applications for spring semester. All application materials must be received by the deadline. Late applications are not accepted or reviewed. 

Applications will be reviewed holistically, using a rubric that considers academic preparation, research experience, contributions to diversity and community, initiative and motivation, and synergy with the program, each evaluated in the context of the individual applicant.

For more information please visit:

• Which Program is Right For You
• Steps to a PhD
• Application Requirements
• Review Process

Areas of Neuroscience

The Neuroscience PhD Program provides research training in four broad areas of neuroscience: cellular & molecular neuroscience, circuit, systems & behavioral neuroscience, human cognition, and computational neuroscience. Read more about each below.

Molecular & Cellular Neuroscience

Molecular and cellular neuroscientists at Berkeley study neuronal cell biology, cellular physiology, and molecular and genetic basis of neuron, synapse, and glial function. Specific topics include sensory transduction, cellular-level neuronal development, synaptic transmission and plasticity, ion channel physiology, neurodegenerative disease, and neurodevelopmental disorders. Many faculty develop novel molecular genetic tools to more precisely measure cellular physiology or to develop new therapeutical approaches to disease. Methods from molecular biology, computational biology (bioinformatics), and cellular physiology are often used in this research.

Circuit, Systems & Behavioral Neuroscience

Circuit, systems and behavioral neuroscientists at UC Berkeley study how neural circuits, ensembles, and large-scale neural systems process information in order to interpret the sensory world, make and recall memories, and produce specific behaviors. Our faculty study neural systems for sensory processing (vision, audition, touch), innate behaviors, memory, navigation, motivated behaviors, sleep, circadian rhythms, social behaviors, decision making, and more. This research often involves neurophysiology, imaging, and optogenetics experiments, usually in behaving animals. Computational models of neural circuits, and sophisticated data analysis involving modeling and machine learning, are often used in this research.

Cognitive Neuroscience

Cognitive neuroscience at UC Berkeley focuses on human cognition and its brain correlates. Our faculty study the human cognitive abilities and neural mechanisms underlying learning and memory, decision making, perception, reasoning, attention, sleep, motor control, etc. Berkeley human cognition labs employ a broad range of experimental techniques, including functional and structural neuroimaging, electrophysiology, brain stimulation, pharmacology, computational modeling, and quantitative behavioral analyses.

Computational Neuroscience

Although quantitative methods are used in all sub areas of neuroscience for analyzing complex data sets, the focus of Computational Neuroscience is to model the brain or brain function: computational models can attempt to model experimental data obtained in neurophysiological experiments (biophysically plausible models) or model functions achieved by the brain such as object recognition, language comprehension, symbolic manipulations, etc. A strong mathematical and programming background is required for research in Computational Neuroscience.

Please see the Neuroscience Department page:  Diversity, Equity & Inclusion .

Recorded Info Session:

Friday, November 3, 2023 11am-12pm Pacific Time Neuroscience PhD Program – Diversity Admissions Fair Info Session Hosted by Program Faculty with Current Students Session Recording

Previously Recorded Info Session:

Friday, November 4, 2022 10-11am Pacific Time AMA Grad Student Panel for Prospective Applicants Hosted by Current Students Session Recording

Best Global Universities for Neuroscience and Behavior

The field of neuroscience and behavior deals with many subjects, all of which relate to the study of the brain and nervous system. Students learn about topics including molecular psychiatry, neuronal function, basic and clinical neurology, neuronal development, and cellular and molecular neuroscience. These are the world's top universities for neuroscience and behavior. Read the methodology »

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Here are the best global universities for neuroscience and behavior

Harvard university, university of california san francisco, massachusetts institute of technology (mit), stanford university, johns hopkins university, university of pennsylvania, university college london, columbia university, washington university (wustl), yale university.

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• # 1 in Best Universities for Neuroscience and Behavior
• # 1 in Best Global Universities

Founded in 1636, Harvard University is the oldest higher education institution in the U.S. The bulk of Harvard's... Read More

• # 2 in Best Universities for Neuroscience and Behavior
• # 16 in Best Global Universities  (tie)

The University of California—San Francisco is a public institution that was founded in 1864. The health sciences-focused... Read More

• # 3 in Best Universities for Neuroscience and Behavior
• # 2 in Best Global Universities

Massachusetts Institute of Technology, founded in 1861, is located in Cambridge, Massachusetts, near Boston. Around... Read More

• # 4 in Best Universities for Neuroscience and Behavior
• # 3 in Best Global Universities

Stanford University was founded in 1885 and is located in California’s Bay Area, around 30 miles south of San Francisco... Read More

• # 5 in Best Universities for Neuroscience and Behavior
• # 10 in Best Global Universities

Johns Hopkins University is a private institution that was founded in 1876. The school has campuses located in and... Read More

• # 6 in Best Universities for Neuroscience and Behavior
• # 15 in Best Global Universities

The University of Pennsylvania, also known as Penn, was founded in 1740. The private, Ivy League institution is located... Read More

• # 7 in Best Universities for Neuroscience and Behavior
• # 12 in Best Global Universities

University College London, or UCL, is a public institution that was founded in 1826. It was the third university... Read More

• # 8 in Best Universities for Neuroscience and Behavior
• # 7 in Best Global Universities

Columbia University is a private institution that was founded in 1754. It is located in the Upper West Side of New York... Read More

• # 9 in Best Universities for Neuroscience and Behavior
• # 32 in Best Global Universities

Washington University in St. Louis, also known as Wash U, is a private institution that was founded in 1853. The... Read More

• # 10 in Best Universities for Neuroscience and Behavior
• # 11 in Best Global Universities

Yale University was founded in 1701, making it one of the oldest institutions of higher education in the U.S. The... Read More

Gatsby Computational Neuroscience Unit MPhil/PhD

London, Bloomsbury

The Gatsby Unit PhD programme was the first to combine theoretical neuroscience and machine learning within the same programme. Our mathematical approach for developing novel algorithms and tools to understand learning, perception and action in brain and machines is unique.  Applications to this programme must be submitted directly to the Gatsby Unit via its online portal.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

Applications closed

• Entry requirements

Applicants must have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree at a minimum of upper second-class UK Bachelor's level or an overseas equivalent, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.

The English language level for this programme is: Level 3

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance . This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

The Gatsby Unit is a world-class research centre for Computational and Theoretical Neuroscience and Machine Learning. Our research seeks to understand the principles of learning, perception and action in brains and machines by developing mathematical algorithms. We provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with other UCL research groups, in particular the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC) as well as the Centre for Computational Statistics and Machine Learning (CSML). Teaching is supplemented with regular research talks, journal clubs and reading groups, external seminar series and participation in relevant conferences and workshops. PhD students will be supported by all academic staff, not just their immediate supervisors.

Who this course is for

Applicants should have a strong analytical and mathematical background, a keen interest in neuroscience and/or machine learning, and a first degree in relevant fields such as Computer science, Engineering, Physics, Mathematics, Statistics, Neuroscience, or Cognitive Psychology. Many applicants to our programme will have a Master’s degree, but this is not essential. Each application is assessed on its individual merits. Candidates offered a place on our programme will be required to meet UCL's standard admissions requirements (including the English language proficiency requirements for international applicants).

What this course will give you

Students at the Gatsby Computational Neuroscience Unit study toward a PhD in Theoretical Neuroscience and Machine Learning. The Gatsby Unit is part of the Centre for Computational Statistics and Machine Learning, together with UCL Computer Science and Statistical Science. Approximately 90% of alumni have secured academic or industry positions. Many of the most successful leaders in the fields of computational neuroscience and machine learning have studied or worked in the unit, and the unit has a reputation for offering world-class training.

Our programme offers first-year teaching in Systems and Theoretical Neuroscience (jointly with SWC), Probabilistic and Unsupervised Learning, Approximate Inference and Learning in Probabilistic Models, Theoretical Neuroscience, and Advanced Methods in Machine Learning. In your second year, you will carry out a 2-month rotation project in a lab of your choice, either at or external to UCL. The rotation project should be on a research area not related to your PhD topic. Our course also offers a variety of career and skills development training. 

We are based in a purpose-designed building in the heart of London and UCL. The building also houses the SWC, with which we interact closely. Our building offers two large seminar rooms, common areas for social interaction or quiet study, high-performance computing facilities, kitchen facilities, and a brasserie offering breakfast, lunch, coffee and snacks. There are also two large outdoor areas and an on-site bicycle rack. The surrounding area is vibrant with a variety of shops, restaurants, cafes and bars, and museums.

The foundation of your career

Students receive world-class training through our intense, rigorous and diverse research programme. As a result, the majority are highly employable. Students will develop interdisciplinarity and will be expected to collaborate widely. You will develop your communication skills by giving regular internal talks as well as writing scientific publications. Critical thinking is further developed through weekly journal clubs and lab meetings. You will be expected to teach during the second year of the programme and act as a mentor to junior PhDs. Participation in outreach and public engagement activities is also encouraged.

Employability

Most of our graduates have continued in the fields of computational neuroscience and/or machine learning. Alumni have secured academic positions in prestigious institutions such as Cambridge, Oxford, Edinburgh, Columbia, Princeton, Caltech, École Normale Supérieure, the Max Planck Institute for Intelligent Systems and Janelia Research Campus, or have gone on to work in companies such as Google DeepMind, Amazon, Facebook, Samsung and Babylon Health.

There are many networking opportunities, both within and external to the Gatsby Unit. Within the unit, there are daily tea hours where members take turns to present their research or a 10-minute talk on an interesting topic to the unit. Our external seminar series provides an opportunity to meet and interact with eminent speakers and external attendees. 

Based in the same building as the SWC, students will have the opportunity to network extensively with experimentalists and to attend SWC seminars, lab meetings and joint social events. Through CSML you will network with peers in Computer Science and Statistics. All students are encouraged to attend relevant conferences and workshops.

Teaching and learning

The four-year PhD programme includes in its first year intensive courses that provide a comprehensive introduction to theoretical and systems neuroscience and machine learning:

• Systems Neuroscience and Theoretical Neuroscience
• Probabilistic and Unsupervised Learning (COMP0086)
• Approximate Inference and Learning in Probabilistic Models (COMP0085)
• Advanced Topics in Machine Learning (COMP0083)
• Reinforcement Learning (COMP0089)

Students will also be required to carry out a 2-month rotation project in a field different from the field of their PhD thesis.

We offer a supportive and interdisciplinary environment with close links to the SWC and the ELLIS Unit at UCL. For more details see https://www.ucl.ac.uk/gatsby/study-and-work/phd-programme/programme-structure.

Students are required to complete coursework for all first-year courses. There are three examinations in the first year of study.

Typical contact hours are 9am - 5pm.

Research areas and structure

• Analysis of neural data
• Neural dynamics
• Neural plasticity
• Perceptual processing of auditory and visual input
• Neural population coding
• Kernel methods
• Bayesian statistics
• Reinforcement learning
• Statistical machine learning
• Unsupervised learning
• Network and relational data

Research environment

The Gatsby Computational Neuroscience Unit was established by the Gatsby Charitable Foundation at UCL in 1998 to provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with other UCL research groups in neuroscience, machine learning and related areas. We are one of the first centres in the world to bring together the fields of theoretical neuroscience and machine learning, and our investigators have pioneered  research  into the mathematical underpinnings of learning, perception and action in natural and artificial systems.

In 2016 we moved from our original home in Queen Square to Fitzrovia to create a new collaborative partnership with the  Sainsbury Wellcome Centre for Neural Circuits and Behaviour  (SWC). The SWC and the Gatsby Unit work together closely, with joint research appointments, parallel PhD programmes, common day-to-day activities, and joint research projects that bring together theoretical and experimental neuroscience. We are also part of the cross-faculty Centre for Computational Statistics and Machine Learning (CSML) and work with other UCL research groups in neuroscience  and machine learning . 

There are many networking opportunities both within and external to the Gatsby Unit. Within the unit, there are regular talks where members take turns to give research talks to the rest of the unit. The unit also holds regular seminars to which eminent speakers are invited; these are open to other members of the university, and there is an opportunity to meet speakers and attendees after the talk. Students also run journal clubs and their own reading groups, and all students have the opportunity to attend relevant conferences and workshops.

The PhD programme lasts for four years, including first-year teaching  in techniques and research in theoretical and systems neuroscience and machine learning. We only admit new students starting in September.

Courses in the first year, taught with colleagues from the SWC and CSML, provide a comprehensive introduction to theoretical and systems neuroscience and to machine learning; with multidisciplinary training in other areas of neuroscience also available. Students are encouraged to work and interact closely with peers and faculty in the SWC and CSML throughout their PhD to take advantage of this uniquely multidisciplinary research environment.

In Year 1, students take core courses in theoretical neuroscience (TN), systems neuroscience (taught with SWC) and machine learning (ML), after which they generally choose to concentrate on either TN or ML. Students sit exams for TN and ML during the second (third) term. 

In Year 2, students are expected to work on their thesis project. Students also do a 2-month rotation in a field that is not related to their PhD thesis. At the end of the second year, students are expected to write an MPhil/PhD upgrade report on their progress and future plans and schedule to give a presentation on this report to the rest of the unit. Having passed all the required assessments, students then transfer from MPhil to PhD status and devote their remaining tenure to research. 

Throughout the PhD programme, students are immersed in a highly stimulating educational environment, comprising regular talks, research reports, journal clubs and ad hoc reading groups; seminar series in the unit and other UCL departments (including  SWC ;  Institute of Cognitive Neuroscience ;  Institute of Neurology , Department of Psychology ;  Neuroscience, Physiology and Pharmacology ;  the ELLIS Unit at UCL ; and  Department of Statistical Science ), and participation in international conferences such as COSYNE, ICML and NeurIPS.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

There are no programme-specific additional costs.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

A number of fully-funded studentships are available each year. Each studentship covers payment of full fees, provides a generous tax-free living stipend and a travel budget for conference/workshop attendance. Studentships are available to students of any nationality.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

All applications must be made directly to the Gatsby Unit by the deadline stated on the Gatsby Unit website. Please refer to this web page on the Gatsby Unit website for how to apply to this programme. Late applications will be held until all applications received by the deadline have been assessed. It is very likely that all available positions will be filled by then.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Got questions? Get in touch

Gatsby Computational Neuroscience Unit

[email protected]

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Best Universities for Neuroscience in the World

Updated: February 29, 2024

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Below is a list of best universities in the World ranked based on their research performance in Neuroscience. A graph of 246M citations received by 8.23M academic papers made by 5,102 universities in the World was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Harvard University

For Neuroscience

2. University College London

3. Johns Hopkins University

4. Stanford University

5. Yale University

6. University of Toronto

7. University of California - San Francisco

8. University of Pennsylvania

9. Columbia University

10. University of California-San Diego

11. University of California - Los Angeles

12. University of Michigan - Ann Arbor

13. University of Washington - Seattle

14. University of Pittsburgh

15. University of Oxford

16. McGill University

17. Washington University in St Louis

18. University of Cambridge

19. Karolinska Institute

20. New York University

21. Cornell University

22. University of Wisconsin - Madison

23. University of British Columbia

24. Emory University

25. Northwestern University

26. Massachusetts Institute of Technology

27. King's College London

28. University of California - Berkeley

29. University of California - Irvine

30. University of Minnesota - Twin Cities

31. University of Melbourne

32. Boston University

33. University of North Carolina at Chapel Hill

34. Heidelberg University - Germany

35. University of Southern California

36. University of Iowa

37. University of Tokyo

38. Duke University

39. Baylor College of Medicine

40. University of Florida

41. Pierre and Marie Curie University

42. University of Chicago

43. University of California - Davis

44. Rutgers University - New Brunswick

45. University of Sydney

46. Case Western Reserve University

47. Ohio State University

48. University of Texas Southwestern Medical Center

49. Vanderbilt University

50. University of Illinois at Urbana - Champaign

51. Kyoto University

52. Icahn School of Medicine at Mount Sinai

53. Mayo Clinic College of Medicine and Science

54. Radboud University

55. University of Zurich

56. Oregon Health & Science University

57. University of Montreal

58. Lund University

59. University of Amsterdam

60. University of Miami

61. University of Edinburgh

62. University of Munich

63. University of Virginia

64. Rockefeller University

65. University of New South Wales

66. Catholic University of Leuven

67. University of Utah

68. Pennsylvania State University

69. Western University

70. University of Manchester

71. University of Arizona

72. Tel Aviv University

73. University of Queensland

74. University of Calgary

75. Charite - Medical University of Berlin

76. University of Tubingen

77. Osaka University

78. University of Illinois at Chicago

79. University of Sao Paulo

80. University of Rochester

81. University of Copenhagen

82. Imperial College London

83. University of Alabama at Birmingham

84. University of Maryland, Baltimore

85. Brown University

86. Monash University

87. University of Kentucky

88. University of Texas at Austin

89. University of Milan

90. University of Alberta

91. University of Groningen

92. University of Gothenburg

93. University of Bristol

94. Princeton University

95. University of Helsinki

96. Indiana University - Purdue University - Indianapolis

97. California Institute of Technology

98. Providence College

99. Sapienza University of Rome

100. Wayne State University

Biology subfields in the World