physics phd demographics

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The Survey of Earned Doctorates is an annual census conducted since 1957 of all individuals receiving a research doctorate from an accredited U.S. institution in a given academic year. The SED is sponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF) and by three other federal agencies: the National Institutes of Health, Department of Education, and National Endowment for the Humanities. The SED collects information on the doctoral recipient’s educational history, demographic characteristics, and postgraduation plans. Results are used to assess characteristics of the doctoral population and trends in doctoral education and degrees.

Areas of Interest

• STEM Education
• Science and Engineering Workforce

Survey Administration

The 2022 survey was conducted by RTI International under contract to NCSES.

Survey Details

Featured survey analysis.

Doctorate Recipients from U.S. Universities: 2022

SED Overview

Data highlights, the number of research doctorates conferred by u.s. institutions, which began a sharp 15-month decline in spring 2020 due to the covid-19 pandemic, rebounded in 2022 with the highest number of research doctorates awarded in any academic year to date.

Over the past 20 years, most of the growth in the number of doctorates earned by both men and women has been in science and engineering (S&E) fields 

Methodology

Survey description, technical notes, technical tables, questionnaires, view archived questionnaires, featured analysis.

Research Doctorate Conferrals Rebound, Leading to Record Number of U.S. Doctorate Recipients in 2022

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For additional information about this survey or the methodology, contact

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Many PhD students in the MIT Physics Department incorporate probability, statistics, computation, and data analysis into their research. These techniques are becoming increasingly important for both experimental and theoretical Physics research, with ever-growing datasets, more sophisticated physics simulations, and the development of cutting-edge machine learning tools. The Interdisciplinary Doctoral Program in Statistics (IDPS)  is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.

Admission to this program is restricted to students currently enrolled in the Physics doctoral program or another participating MIT doctoral program. In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods. Graduates of the program will receive their doctoral degree in the field of “Physics, Statistics, and Data Science.”

Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

For access to the selection form or for further information, please contact the IDSS Academic Office at  [email protected] .

Required Courses

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

• IDS.190 – Doctoral Seminar in Statistics and Data Science ( may be substituted by IDS.955 Practical Experience in Data, Systems and Society )
• 6.7700[J] Fundamentals of Probability or
• 18.675 – Theory of Probability
• 18.655 – Mathematical Statistics or
• 18.6501 – Fundamentals of Statistics or
• IDS.160[J] – Mathematical Statistics: A Non-Asymptotic Approach
• 6.C01/6.C51 – Modeling with Machine Learning: From Algorithms to Applications or
• 6.7810 Algorithms for Inference or
• 6.8610 (6.864) Advanced Natural Language Processing or
• 6.7900 (6.867) Machine Learning or
• 6.8710 (6.874) Computational Systems Biology: Deep Learning in the Life Sciences or
• 9.520[J] – Statistical Learning Theory and Applications or
• 16.940 – Numerical Methods for Stochastic Modeling and Inference or
• 18.337 – Numerical Computing and Interactive Software
• 8.316 – Data Science in Physics or
• 6.8300 (6.869) Advances in Computer Vision or
• 8.334 – Statistical Mechanics II or
• 8.371[J] – Quantum Information Science or
• 8.591[J] – Systems Biology or
• 8.592[J] – Statistical Physics in Biology or
• 8.942 – Cosmology or
• 9.583 – Functional MRI: Data Acquisition and Analysis or
• 16.456[J] – Biomedical Signal and Image Processing or
• 18.367 – Waves and Imaging or
• IDS.131[J] – Statistics, Computation, and Applications

Grade Policy

C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated.

Unless approved by the PhysSDS co-chairs, a minimum grade of B+ is required in all 12 unit courses, except IDS.190 (3 units) which requires a P grade.

Though not required, it is strongly encouraged for a member of the MIT  Statistics and Data Science Center (SDSC)  to serve on a student’s doctoral committee. This could be an SDSC member from the Physics department or from another field relevant to the proposed thesis research.

Thesis Proposal

All students must submit a thesis proposal using the standard Physics format. Dissertation research must involve the utilization of statistical methods in a substantial way.

PhysSDS Committee

• Jesse Thaler (co-chair)
• Mike Williams (co-chair)
• Isaac Chuang
• Janet Conrad
• William Detmold
• Philip Harris
• Jacqueline Hewitt
• Kiyoshi Masui
• Leonid Mirny
• Christoph Paus
• Phiala Shanahan
• Marin Soljačić
• Washington Taylor
• Max Tegmark

Can I satisfy the requirements with courses taken at Harvard?

Harvard CompSci 181 will count as the equivalent of MIT’s 6.867.  For the status of other courses, please contact the program co-chairs.

Can a course count both for the Physics degree requirements and the PhysSDS requirements?

Yes, this is possible, as long as the courses are already on the approved list of requirements. E.g. 8.592 can count as a breadth requirement for a NUPAX student as well as a Data Analysis requirement for the PhysSDS degree.

If I have previous experience in Probability and/or Statistics, can I test out of these requirements?

These courses are required by all of the IDPS degrees. They are meant to ensure that all students obtaining an IDPS degree share the same solid grounding in these fundamentals, and to help build a community of IDPS students across the various disciplines. Only in exceptional cases might it be possible to substitute more advanced courses in these areas.

Can I substitute a similar or more advanced course for the PhysSDS requirements?

Yes, this is possible for the “computation and statistics” and “data analysis” requirements, with permission of program co-chairs. Substitutions for the “probability” and “statistics” requirements will only be granted in exceptional cases.

For Spring 2021, the following course has been approved as a substitution for the “computation and statistics” requirement:   18.408 (Theoretical Foundations for Deep Learning) .

The following course has been approved as a substitution for the “data analysis” requirement:   6.481 (Introduction to Statistical Data Analysis) .

Can I apply for the PhysSDS degree in my last semester at MIT?

No, you must apply no later than your penultimate semester.

What does it mean to use statistical methods in a “substantial way” in one’s thesis?

The ideal case is that one’s thesis advances statistics research independent of the Physics applications. Advancing the use of statistical methods in one’s subfield of Physics would also qualify. Applying well-established statistical methods in one’s thesis could qualify, if the application is central to the Physics result. In all cases, we expect the student to demonstrate mastery of statistics and data science.

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Toni Feder; Graduate demographics in the US. Physics Today 1 April 2014; 67 (4): 27. https://doi.org/10.1063/PT.3.2345

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How many women are earning advanced physics degrees? How many people are from underrepresented groups? How long is it taking people to earn their degrees? The makeup of US exiting-master’s and PhD recipients over time, with an in-depth look at the classes of 2010, 2011, and 2012, are the subjects of two recent reports by the Statistical Research Center at the American Institute of Physics. An exiting master’s is someone who leaves his or her physics department after earning a master’s degree—including those who continue studies in physics elsewhere or switch to another field.

In 2012 a new high of 1762 PhDs were awarded in physics. That number is up 4% from the previous year and 62% from a recent low in 2004. The median number of physics PhDs awarded by departments went from four to six in that period. On average, PhD recipients in the classes of 2010 and...

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• Published: 08 January 2019

Taking census of physics

• Federico Battiston 1   na1 ,
• Federico Musciotto 1   na1 ,
• Dashun Wang   ORCID: orcid.org/0000-0002-7054-2206 2 , 3 ,
• Albert-László Barabási 1 , 4 , 5 ,
• Michael Szell   ORCID: orcid.org/0000-0003-3022-2483 1 , 4 , 6 &
• Roberta Sinatra   ORCID: orcid.org/0000-0002-7558-1028 1 , 4 , 7 , 8  

Nature Reviews Physics volume  1 ,  pages 89–97 ( 2019 ) Cite this article

10k Accesses

44 Citations

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• Complex networks
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Over the past decades, the diversity of areas explored by physicists has exploded, encompassing new topics from biophysics and chemical physics to network science. However, it is unclear how these new subfields emerged from the traditional subject areas and how physicists explore them. To map out the evolution of physics subfields, here, we take an intellectual census of physics by studying physicists’ careers. We use a large-scale publication data set, identify the subfields of 135,877 physicists and quantify their heterogeneous birth, growth and migration patterns among research areas. We find that the majority of physicists began their careers in only three subfields, branching out to other areas at later career stages, with different rates and transition times. Furthermore, we analyse the productivity, impact and team sizes across different subfields, finding drastic changes attributable to the recent rise in large-scale collaborations. This detailed, longitudinal census of physics can inform resource allocation policies and provide students, editors and scientists with a broader view of the field’s internal dynamics.

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Acknowledgements

This work was supported by the John Templeton Foundation Grant #61066 (A.-L.B., F.B., R.S. and M.S.), the Intellectual Themes Initiative (ITI) project ‘Just Data’, funded by Central European University (F.M. and R.S.), the National Science Foundation grant SBE 1829344 (D.W.) and the Air Force Office of Scientific Research grants FA9550-15-1-0077 (A.-L.B., R.S. and M.S.), FA9550-15-1-0364 (A.-L.B. and R.S.), FA9550-15-1-0162 (D.W.) and FA9550-17-1-0089 (D.W.).

Author information

These authors contributed equally: Federico Battiston, Federico Musciotto

Authors and Affiliations

Department of Network and Data Science, Central European University, Budapest, Hungary

Federico Battiston, Federico Musciotto, Albert-László Barabási, Michael Szell & Roberta Sinatra

Kellogg School of Management, Northwestern University, Evanston, IL, USA

Dashun Wang

Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA

Network Science Institute, Northeastern University, Boston, MA, USA

Albert-László Barabási, Michael Szell & Roberta Sinatra

Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA

Albert-László Barabási

MTA KRTK Agglomeration and Social Networks Lendulet Research Group, Centre for Economic and Regional Studies, Hungarian Academy of Sciences, Budapest, Hungary

Michael Szell

Department of Mathematics, Central European University, Budapest, Hungary

Roberta Sinatra

ISI Foundation, Torino, Italy

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Contributions

A.-L.B., R.S., M.S. and D.W. conceived the study. All authors designed the research, discussed the results and commented on the manuscript. F.B., F.M. and R.S. developed the methods. F.B. and F.M. analysed the data. M.S. and R.S. directed the research. F.B., F.M., M.S. and R.S. led the writing of the manuscript and A.-L.B. and D.W. edited the manuscript. F.B. and F.M. wrote the supplementary information.

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Correspondence to Roberta Sinatra .

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Battiston, F., Musciotto, F., Wang, D. et al. Taking census of physics. Nat Rev Phys 1 , 89–97 (2019). https://doi.org/10.1038/s42254-018-0005-3

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Published : 08 January 2019

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DOI : https://doi.org/10.1038/s42254-018-0005-3

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Graduate education in physics offers you exciting opportunities extending over a diverse range of subjects and departments. You will work in state-of-the-art facilities with renowned faculty and accomplished postdoctoral fellows. The interdisciplinary nature of the program provides you with the opportunity to select the path that most interests you. You will be guided by a robust academic advising team to ensure your success.

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Students in the program are doing research in many areas, including atomic and molecular physics, quantum optics, condensed-matter physics, computational physics, the physics of solids and fluids, biophysics, astrophysics, statistical mechanics, mathematical physics, high-energy particle physics, quantum field theory, string theory, relativity, and many others.

Graduates of the program have secured academic positions at institutions such as MIT, Stanford University, California Institute of Technology, and Harvard University. Others have gone into private industry at leading organizations such as Google, Facebook, and Apple. 

Additional information on the graduate program is available from the Department of Physics , and requirements for the degree are detailed in Policies . 

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Applicants should be well versed in undergraduate-level physics and mathematics. Typically, applicants will have devoted approximately half of their undergraduate work to physics and related subjects such as mathematics and chemistry. It is desirable for every applicant to have completed at least one year of introductory quantum mechanics classes. An applicant who has a marked interest in a particular branch of physics should include this information in the application. If possible, applicants should also indicate whether they are inclined toward experimental or theoretical (mathematical) research. This statement of preference will not be treated as a binding commitment to any course of study and research. In the Advanced Coursework section of the online application, prospective students must indicate the six most advanced courses (four in physics and two in mathematics) they completed or will complete at their undergraduate institution.

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• Outcomes data is based on students who completed a PhD between academic years 2010-11 to 2014-15
• Student enrollments in and degrees conferred by the joint MD/PHD programs with the Pritzker School of Medicine are not included in these reports.
• Data in these charts follow U.S. reporting requirements, which currently allow only “male” and “female” as gender categories.
• International is defined by IPEDS as nonresident alien. View IPEDS'  Definitions for New Race and Ethnicity Categories .

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This graph shows the number of physics PhDs earned by US citizens and permanent residents as compared to physics PhDs earned by temporary residents.

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Physics has been taught at the University of Michigan since the autumn of 1843, under the name of "Natural Philosophy." At the time, the program consisted of eleven college juniors and two faculty members. The Physics Department, understandably, looks a lot different today. Housed in Randall and Homer A. Neal Laboratories on U-M Central Campus, the department's faculty of over fifty professors and lecturers instruct thousands of students a term under a diverse catalog of courses. Our graduate program, typically consisting of about 150 students, is central to the service, education, and community the program provides. Physics PhD students undergo five years of academic and professional training to earn their degree, all while participating on the frontline of new and exciting research. 

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We fully recognize that our current gender and racial demographics are influenced by and reflect historical inequities both inside and outside our physics community. While our demographics are comparable to or slightly more equalized than that of the general physics community, we are still far from our goal. To this end, we are constantly working towards making our physics community more accessible, equitable, and inclusive. See our Physics DEI webpage for more information about some of these initiatives. 

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Physics Ph.D. student perspectives on the importance and difficulty of finding a research group

Mike verostek, casey w. miller, and benjamin m. zwickl, phys. rev. phys. educ. res. 20 , 010136 – published 7 may 2024, see research news: alleviating the stress of finding a phd advisor.

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Supplemental Material

• INTRODUCTION AND BACKGROUND
• DISCUSSION AND FUTURE WORK
• ACKNOWLEDGMENTS

Joining a research group is one of the most important events on a graduate student’s path to becoming an independent physics researcher and earning a Ph.D. However, graduate students’ perspectives on the experience of finding a research group are not well documented in the literature. Understanding these perspectives is crucial for evaluating whether departments are providing students with adequate support while they search for a research group, and how difficulties during this process contribute to attrition. Semistructured interviews with N = 20 first and second year physics Ph.D. students reveal that incoming graduate students see joining a research group as a significant decision, and recognize that it may impact whether they will be able to complete the program. We found that students who struggled to find a group felt isolated and worried about falling behind their peers, whereas students who were able to immerse themselves in a positive group environment reported increased sense of belonging in their programs. The process of finding a research group often held differential importance for students identifying as women and nonbinary, who at times reported having to deprioritize their preferred research topic in order to be part of a more inclusive working environment. Although incoming graduate students characterized joining a research group as a significant decision, they often felt unprepared to make it. Moreover, they perceived an overall lack of guidance and structure from their departments, and characterized coursework as a barrier to searching for a group. Our findings suggest that providing students with better support during their group search process could help improve retention, particularly for traditionally underrepresented students, and improve students’ overall satisfaction in their graduate programs.

• Received 7 November 2023
• Accepted 14 March 2024

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.20.010136

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

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Research News

Alleviating the Stress of Finding a PhD Advisor

Published 7 may 2024.

At many US universities, no formal procedure exists to help physics students pick a PhD project and a supervisor. Researchers argue it’s time for that to change.

See more in Physics

Authors & Affiliations

• Department of Physics and Astronomy, University of Rochester, Rochester, New York 14627, USA and School of Physics and Astronomy, Rochester Institute of Technology, Rochester, New York 14623, USA
• School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, New York 14623, USA
• School of Physics and Astronomy, Rochester Institute of Technology, Rochester, New York 14623, USA
• * [email protected]

Article Text

Vol. 20, Iss. 1 — January - June 2024

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A major theme that emerged was the overall importance of the group search process in students’ doctoral experience. Students perceived choosing a group as a significant decision in their graduate careers. Interviewees who identified as women or nonbinary described finding an inclusive group as a particularly high priority, which we observed could restrict access to certain research opportunities. We also observed that navigating the search process could have major impacts on students’ sense of belonging in the program.

An overarching theme was that students experienced many difficulties while finding a group. Students sought more structure and guidance from departments, particularly regarding ways to connect with faculty and graduate students. Improved guidance from faculty might have helped alleviate several other difficulties, including feeling unprepared for the search process and perceiving that their coursework was in tension with their ability to find a group.

A summary of suggestions to address challenges physics Ph.D. students faced while searching for a research group. Items are sorted by approximate level of effort required by the individual stakeholder to enact, relative to the other items in their list. For instance, departments could adopt a policy to have first-year students complete an existing STEM-IDP with significantly less effort than enacting a formal research rotation program.

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Physical Review Physics Education Research

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Demographics of physics education research

Stephen kanim and ximena c. cid, phys. rev. phys. educ. res. 16 , 020106 – published 27 july 2020.

• Citing Articles (75)
• INTRODUCTION
• LIMITATIONS OF THIS STUDY
• ACKNOWLEDGMENTS

Is physics education research based on a representative sample of students? To answer this question we skimmed physics education research papers from three journals for the years 1970–2015 looking for the number of research subjects, the course the subjects were enrolled in, and the institution where the research was conducted. We combined this data with demographics data about these institutions to compile a profile of physics education research subjects, and compared the demographics of this population to those of all students taking physics in the United States. Our results suggest that physics education research subjects, as a whole, are better prepared mathematically and are from a narrow and unrepresentative subset of our intended target physics student populations. For this reason, findings from research may not be as generalizable to all student populations as we have previously assumed.

• Received 28 April 2020
• Accepted 1 May 2020

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.16.020106

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

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Authors & Affiliations

• Department of Physics, New Mexico State University, Las Cruces, New Mexico 88005, USA
• Department of Physics, California State University Dominguez Hills, Carson, California 90747, USA
• * [email protected]
• † [email protected]

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Vol. 16, Iss. 2 — July - December 2020

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(a) High school physics enrollment compared to physics enrollment at colleges and universities. (b) Enrollment in university courses broken down by type of course. Data for both graphs are from AIP statistics.

Comparison of distribution of research subjects in all post-secondary introductory courses to distribution of all introductory students at colleges and universities in our study. Here we have included students in honors introductory physics courses with students in calculus-based physics. About one-third of all students taking post-secondary introductory physics in the U.S. take a calculus-based course at a four-year institution. In contrast, more than four-fifths of all students reported on in the research papers of our study were in the calculus-based course at a four-year institution. About one-quarter of all students taking introductory physics (of all varieties) do so at a two-year college; only 0.3% of the students in our study were from two-year colleges.

Comparison of middle half SAT Math scores for all students with those of incoming freshmen at universities where most PER is conducted. The blue-shaded histogram gives the distribution of SAT Math scores for all students taking the SAT in 2008 [ 22 ], the last year that the College Board released these data in 50-point bins. The dashed vertical blue lines represent the 25th percentile score (430) and the 75th percentile (590) scores for all students; with the middle half shaded a darker blue than the lowest and highest quarters. The 39 stacked rectangles shaded in red are the 25th–75th percentile ranges for incoming freshmen at the universities whose students comprise 95% of all university students in our study. The horizontal span of each rectangle in the stack represents the middle half of incoming freshman students for a single university, with the height of that rectangle giving the proportional representation from that university of all students in our study.

Binned comparison of SAT Math scores for all college-bound students with students at universities where most PER is conducted. The blue bars are the SAT Math scores for all students who took the SAT in Spring 2016, collected into 100-point bins and given as a percent of all students taking the exam. The red bars are for students at the 39 universities where most PER is conducted, weighted by the contribution of each university to the total number of students in our sample.

Comparison of racial or ethnic distributions of all students taking the SAT in 2015 versus the PER research population from our study. Data and category titles are taken from the College Board website.

Comparison of parents’ income for research student population (from 37 universities representing 87% of the PER research population) to overall student population.

Our best guess about the overlap between research population and overall physics student population. Based on our knowledge of how SAT Math scores change from incoming freshmen to students in calculus-based physics courses for 2 institutions (shown in yellow), and on the assumption that the scores do not change for the school with the highest scores (also in yellow), the 25th–75th percentile ranges shown in Fig.  3 have been mapped to predicted ranges for students in calculus-based physics courses. Score mapping is done by quadratic fit. The data for incoming freshmen from Fig.  3 is shown outlined in light gray for comparison, as are the limits for the middle half range.

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Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

Grade Requirements:  Students must complete their primary program’s degree requirements along with the IDPS requirements. C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated

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Required Courses:

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

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PHD in Physics

Physics phd program.

Upon completion of the PhD Program, graduates will be able to lead efforts in academia and industry in the areas of condensed matter physics, applied physics and materials science. The graduates receive their degree having made significant contributions to the advancement of knowledge in a particular area of research. Courses and seminars provide necessary background in the basic principles, methods and theories of physics. Initial research emphasis will be in the energy sciences, biophysics, and information sciences with the intent to leverage significant research infrastructure investment recently established under the Small Scale Systems Integration and Packaging Center at Binghamton University.

Most of the basic graduate courses in a student's program should be taken during the first year of residence. Proficiency in Solid State Physics, Quantum Mechanics, Electrodynamics, Statistical Thermodynamics and Communication Skills will be attained through classroom study, research and teaching.

The requirements for the doctoral degree include a total of at least twenty-four credit hours of course study (six to eight courses) and at least twenty-four additional credits of dissertation work. The specific course requirements will be determined in consultation with the student's guidance committee (a committee consisting of three Physics faculty members, one of whom is the student's principal advisor). These course requirements must be approved by the graduate program committee, and will normally include those expected for the Masters degree in Physics.

PhD required courses

These course requirements must be approved by the graduate program committee, and will normally include:

PHYS 522 – Electrodynamics I

PHYS 524 – Quantum Mechanics I

PHYS 527 – Graduate Lab

PHYS 572 – Solid State Physics

PHYS 592 – Communications

PHYS 631 – Statistical Mechanics I

Most of the basic graduate courses in a student's program should be taken during the first year of residence. Proficiency in Solid State Physics, Quantum Mechanics, Electrodynamics, Statistical Thermodynamics and Communication Skills will be attained through classroom study, research and teaching. To advance to doctoral candidacy, each student will be required to demonstrate competency in these core areas via a written Qualifying Exam and through the oral defense of a written research proposal. Students will, under the guidance of a faculty member, conduct independent research publishable in an archival journal, and communicate their results in dissertation and presentation forms (PHYS 592). All students will be required to write a dissertation and defend it in a public oral defense before their guidance committee.

Structure/Duration

Students will generally enroll full-time and complete the degree in four to six years. Typically this will involve two semesters of first year graduate courses and a teaching assistantship in introductory Physics courses. All graduate students in Physics attend and participate in seminars presented by fellow students, faculty, and visiting scientists, and attend professional meetings (PHYS 501). The second year in the program may be seen as transitional, including elective courses and potentially a second year of a teaching assistantship, with a growing focus on a research problem. By the end of the second year, the preliminary examination, including a presentation of a proposed dissertation topic, is completed. Dissertation research, writing a dissertation and a public defense complete the degree requirements.

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Alexey Kolmogorov

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Data science CDT puts industry collaboration at its heart

The Liverpool Centre for Doctoral Training (CDT) for Innovation in Data Intensive Science is bridging disciplines and empowering PhD students to excel in data-intensive research in both industry and academia

Physics is a constantly evolving field – how do we make sure the next generation of physicists receive training that keeps pace with new developments and continues to support the cutting edge of research?

According to Carsten P Welsch , a distinguished accelerator scientist at the  University of Liverpool , in the age of machine learning and AI, PhD students in different physics disciplines have more in common than they might think.

“Research is increasingly data-intensive, so while a particle physicist and a medical physicist might spend their days thinking about very different concepts, the approaches, the algorithms, even the tools that people use, are often either the same or very similar,” says Professor Welsch.

Data science is extremely important for any type of research and will probably outlive any particular research field Professor Welsch

Welsch is the director of the Liverpool Centre for Doctoral Training (CDT) for Innovation in Data Intensive Science (LIV.INNO). Founded in 2022, the CDT is currently recruiting its third cohort of PhD students. Current students are undertaking research that spans medical, environmental, particle and nuclear physics, but their projects are all underpinned by data science. According to Professor Welsch, “Data science is extremely important for any type of research and will probably outlive any particular research field.”

Next-generation PhD training

Carsten Welsch has a keen interest in improving postgraduate education, he was chair of STFC’s Education Training and Careers Committee and a member of the UKRI Skills Advisory Group. When it comes to the future of doctoral training he says “The big question is ‘where do we want UK researchers to be in a few years, across all of the different research areas?’”

He believes that LIV.INNO holds the solution. The CDT aims to give students with data-intensive PhD projects the skills that will enable them to succeed not only in their research but throughout their careers.

Lauryn Eley is a PhD student in the first LIV.INNO cohort who is researching medical imaging. She became interested in this topic during her undergraduate studies because it applied what she had learned in university to real-world situations. “It’s important that I can see the benefits of my work translated into everyday experiences, which I think medical imaging does quite nicely,” she says.

Miss Eley’s project is partnered with medical technology company Adaptix . The company has developed a mobile X-ray device which, it hopes, will enable doctors to produce a high-quality 3D X-ray image more cheaply and easily than with a traditional CT scanner.

Her task is to build a computational model of the X-ray device and investigate how to optimize the images it produces. To generate high-quality results she must simulate millions of X-rays. She says that the data science training she received at the start of the PhD has been invaluable.

From their first year, students attend lectures on data science topics which cover Monte Carlo simulation, high-performance computing, machine learning and AI, and data analysis. Lauryn Eley has an experimental background, and she says that the lectures enabled her to get to grips with the C++ she needed for her research.

Boosting careers with industry placements

Professor Welsch says that from the start, industry partnership has been at the centre of the LIV.INNO CDT. Students spend six months of their PhD on an industrial placement, and Lauryn Eley says that her work with Adaptix has been eye-opening, enabling her to experience first-hand the fast-paced, goal-driven world of industry, which she found very different to academic research.

While the CDT may particularly appeal to those keen on pursuing a career in industry, Professor Welsch emphazises the importance of students delivering high-quality research. Indeed, he believes that LIV.INNO’s approach provides students with the best chance of success in their academic endeavours. Students are taught to use project management skills to plan and deliver their projects, which he says puts them “in the driving seat” as researchers. They are also empowered to take initiative, working in partnership with their supervisors rather than waiting for external guidance.

LIV.INNO builds on a previous programme called the Liverpool Big Data Science Centre for Doctoral Training, which ran between 2017 and 2024. Professor Welsch was also the director of that CDT, and he has noticed that when it comes to partnering with student projects, industry attitudes have undergone a shift.

“When we approached the companies for the first time, you could definitely see that there was a lot of scepticism,” he says. “However, with the case studies from the first CDT, they found it much easier to attract industry partners to LIV.INNO.” Professor Welsch thinks that this demonstrates the benefits that industry-academia partnerships bring to both students and companies.

The first cohort from LIV.INNO are only in their second year, but many of the students from the previous CDT secured full-time jobs from the company where they did their placement. But whatever career path students eventually go down, Carsten Welsch is convinced that the cross-sector experience students get with LIV.INNO sets them up for success, saying “They can make a much better informed decision about where they would like to continue their careers.”

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Skylar Dannoff

Xinyan Wang

Skylar Dannhoff receives the prestigious DOE-NNSA Laboratory Residency Graduate Fellowship

Johan Frenje

May 8, 2024

Skylar Dannhoff, a third-year graduate student in the Department of Physics has been awarded the prestigious 2024 Department of Energy National Nuclear Security Administration Laboratory Residency Graduate Fellowship (DOE NNSA LRGF).

Under supervision of Drs.  Chikang Li and Johan Frenje , Dannhoff studies electric and magnetic fields generated in a hohlraum plasma in an indirect-drive Inertial Confinement Fusion (ICF) experiment, in addition to building nuclear diagnostics for studies of ICF and High Energy Density (HED) plasmas. For her LRGF GF residency, Dannhoff will work under the mentorship of Dr. Chris Walsh at the Lawrence Livermore National Laboratory (LLNL) studying the impact of self-generated electric and magnetic fields in laser-driven ICF experiments. Dannhoff said, “I feel incredible lucky to have been offered the opportunity to work so closely with and learn from the expertise of LLNL scientists. I think this fellowship and my residencies with LLNL will add to both the depth and the broader utility of my research, and I'm grateful to my current and past research advisors, mentors, and group members for their support and encouragement.” Her direct MIT supervisor on this project, Dr. Chikang Li, commented, “The LRGF program provides a unique opportunity allowing Skylar not only to perform comprehensive magnetohydrodynamic (MHD) simulations for her PhD thesis projects, but also get exposed to, and communicate with, scientists at the national laboratories.”

The DOE NNSA LRGF connects students working in fields relevant to the DOE lab system with laboratory scientists with the aim of fostering collaborative research relationships. The program will strengthen these university-laboratory links through an unusual and exciting provision: fellows will work and study in residence at one or more of four approved DOE NNSA facilities for a minimum of two 12-week periods. This residency requirement opens compelling research opportunities to students and their advisors, including access to unique DOE NNSA experimental and computing facilities. Additionally, by training researchers in advanced science and engineering, the fellowship is helping to fill key gaps in the U.S. workforce.

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First Data Science Day at SCI Celebrated on April 19

May 10, 2024

The day kicked off with breakfast and networking, followed by presentations and panels featuring data science professionals. Dean Bruce Childers welcomed the attendees to the event.

“I welcome you to the University’s very first ever Data Science Day. Data is absolutely everywhere - it influences science, healthcare, business, government, and so much more. That’s why it is so important to empower others to not only be able to gather and interpret data, but also to use it responsibly for positive societal impact,” said Childers.

Alfred Spector, visiting scholar at the Massachusetts Institute of Technology (MIT), spoke at the keynote presentation, “Beyond Models – Applying Data Science and AI Effectively.” In his presentation, Spector discussed a framework for addressing challenges and effectively using data science and AI. The three-part framework suggests that there are three key components in applying data science and AI – technical contributors, integrity, and ethics.

“Now that we are solving really complicated problems, and many of you want to be the leaders of the solutions, not just the contributors to a particular technique, you better think broadly,” said Spector. “I think we need to have a really good course of study and a broad set of subjects if we are going to apply this technology effectively. There’s an enormous amount that we need to learn to apply technologies that are essentially solving the most complicated problems that we, as humans, have come up with throughout civilization.”

The keynote presentation was followed by a panel discussion, “Responsible Uses of Data Science in the Workforce – Industry Perspectives,” moderated by Vice Provost for Budget and Analytics Stephen Wisniewski and featuring data science professionals:

• Chris Belasco - Senior Manager, Digital Services and Chief Data Officer for the City of Pittsburgh
• Bridget Fitzpatrick – Manager of Data Science, Dick’s Sporting Goods
• Mary Beth Green – Chief Innovation Officer, Sheetz

The discussion explored responsible uses of data science and how it has transformed workplaces across industries.

“The experience of those in the workforce is essential in understanding how the field of data science is developing and how that will impact our training of the next generation of data scientists,” said Wisniewski.

The final panel discussion of the day, “Women in Data Science – Challenges, Opportunities and Empowerment,” was a continuation of the SheLeads Women’s Forum in March 2023. Moderated by President and CEO of the Pittsburgh Technology Council Audrey Russo, the panel featured:

• Sandra Brandon – Research Liaison and Leader of Strategy and Innovation, Office of the CIO, University of Pittsburgh
• Amanda Brodish – Associate Vice Provost for Data Analytics, University of Pittsburgh
• Rosta Farzan – Associate Dean for Diversity, Equity, and Inclusion; Associate Professor, SCI
• Marcela Gomez – Director of Research Analytics, Office of the Senior Vice Chancellor for Research, University of Pittsburgh
• Nalyn Siripong – Senior Data Scientist, University of Pittsburgh Information Technology

“Data science brings together many different perspectives. You can be a part of data science with different backgrounds and different experiences that you have. What really got me interested in data science is that it’s a field that can accommodate different perspectives. There’s room for everyone to be a part of it,” said Farzan.

The first-ever Data Science Day was a success in showcasing the knowledge and expertise of data science professionals across industries, as emphasized by Michael Colaresi, associate vice provost for data science.

“In responsible data science here at Pitt, data science is about and for people,” said Colaresi.“Today marks an exciting milestone as we continue to grow Pitt’s positive impact in data science.”

Watch recordings of the keynote speech and panels  here .

--Alyssa Morales