university of oxford phd computer science

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The Computer Vision and Machine Learning group's research

Computer Vision and Machine Learning

Our research.

This group, which was formed in 2005 and later moved to University of Oxford in 2013, is led by Professor Philip Torr. It comprises around 25-30 people at any one time.

The aim of the group is to engage in state of the art research into the mathematical theory of computer vision and artificial intelligence, but to keep the mathematical research relevant to the needs of society. Members of the group have won major awards in all the main conferences in the field including the International Conference on Computer Vision (ICCV), CVPR, ECCV, BMVC, NeurIPS as well as various thesis awards for the students, and industrial awards such as best Knowledge Transfer Partnership.

We have collaborated with many exciting high tech companies including Google, Facebook, Microsoft, Sony, Technicolor, Baidu, Huawei, Tencent, Apical, and Sharp. As well as startups such as FiveAI, and Oxsight.

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U.S. News Ranks UT Austin Computer Science Among Best in Graduate Program Rankings

The 2024-2025 rankings tout computer science at The University of Texas at Austin as among the seven best nationally.

U.S. News and World Report graduate rankings

The University of Texas at Austin continues to be one of the premier schools for graduate studies, according to U.S. News & World Report’s partial release of its most recent “Best Graduate Schools.” UT made gains in several disciplines, including computer science.

Overall, the University has 42 graduate schools and specialty programs ranked in the top 10 when combined with previous years, including a dozen in the College of Natural Sciences. The publication updates some of its specialty rankings each year and republishes the most recent rankings in other areas. Additionally, U.S. News delayed release of some rankings.

“These numbers are meaningful. Having more than 40 schools, programs and specialties ranked in the top 10 in this partial release alone, including several that are the best in the country, if not the world, is reflective of our ability to continue to attract exceptional faculty and students,” said President Jay Hartzell. “Our talent is what puts UT at the leading edge of discovery in AI and robotics, life sciences, population research, and many other disciplines that are at the forefront of solving many of the world’s most pressing problems and bettering society.”

The College of Natural Sciences has 12 programs and specialties ranked among the top 10 in the most recent ranking for a discipline, the most of any college or school at UT Austin. Computer Science (No. 7) scored top 10 rankings in all four discipline’s specialties that are ranked within departments. 

U.S. News & World Report’s graduate rankings, which are published separately from the magazine’s yearly ranking of undergraduate programs, are considered the gold standard of graduate and professional rankings. They are based on surveys of academic leaders and, for select programs, additional quantitative measures including placement test scores, student/faculty ratios, research expenditures and job placement success.

UT Austin graduate schools, programs and specialties within Natural Sciences that U.S. News & World Report ranked this year or last are listed below.

Chemistry – 16*

• Analytical – 4*
• Inorganic – 14*
• Organic – 20*
• Physical – 14*

Computer Science – 7

• Artificial Intelligence – 9
• Programming Language – 7
• Systems – 10

Mathematics – 13*

• Algebra / Number Theory / Algebraic Geometry – 19*
• Analysis – 8*
• Applied Math – 7*
• Topology – 8*

Physics – 13*

• Cosmology/Relativity/Gravity – 10*
• Condensed Matter – 22*

Statistics – 27* 

* Ranking not revised for 2024-25.   In the most recent life science rankings from U.S. News, UT Austin ranked in the top 25 for biological sciences, including ranking 8th in ecology/evolutionary biology; in the most recent ranking for plasma physics, UT ranked third.

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U.S. News Ranks UT Austin Computer Science Among Best in Graduate Program Rankings

Submitted by Staci R Norman on Tue, 04/09/2024 - 11:00am

The 2024-2025 rankings tout computer science at The University of Texas at Austin as among the seven best nationally.

The UT Computer Science graduate program continues to be recognized as a  top 10 program in the nation , as well as among the top 5 public schools and the best in Texas, according to U.S. News & World Report’s partial release of its most recent “Best Graduate Schools”  released today. The magazine ranks programs in alternating years.

UT Computer Science ranks 7th nationally, tied with Georgia Tech and the University of Washington. Four “ specialties ,” or areas of research, at UTCS also rank in the top ten, with Programming Languages coming in 7th, Theory ranked 8th, Artificial Intelligence ranked 9th, and Systems ranked 10th.

U.S. News has delayed release of rankings in additional areas in which The University of Texas at Austin has historically achieved No. 1 and top 10 rankings. Even with the partial release of the graduate rankings, UT maintained its top 10 spot for five colleges and schools. Overall, the University has 42 graduate schools and specialty programs ranked in the top 10 when combined with previous years.

“Our talent is what puts UT at the leading edge of discovery in AI and robotics, life sciences, population research, and many other disciplines that are at the forefront of solving many of the world’s most pressing problems and bettering society,” said President Jay Hartzell. 

U.S. News & World Report’s graduate rankings, which are published separately from the yearly ranking of undergraduate programs, are considered the gold standard of graduate and professional rankings. They are based on surveys of academic leaders and, for select programs, additional quantitative measures including placement test scores, student/faculty ratios, research expenditures, salary by profession and job placement success.

The publication updates some of its specialty rankings each year and republishes the most recent rankings in other areas.

Read the full UT News Release .

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School of computer science.

Master of Computational Data Science

The Master of Computational Data Science (MCDS) program focuses on engineering and deploying large-scale information systems, and includes concentrations in Systems, Analytics, and Human-Centered Data Science.

Requirements

The MCDS program offers three majors: Systems, Analytics, and Human-Centered Data Science. All three require the same total number of course credits, split among required core courses, electives, data science seminar and capstone courses specifically defined for each major. The degree can also be earned in two different ways, depending on the length of time you spend working on it. Regardless of the timing option, all MCDS students must complete a minimum of 144 units to graduate.

Here are the options:

• Standard Timing — a 16-month degree consisting of study for fall and spring semesters, a summer internship, and fall semester of study. Each semester comprises a minimum of 48 units. This timing is typical for most students. Students graduate in December.
• Extended Timing — a 20-month degree consisting of study for fall and spring semesters, a summer internship, and a second year of fall and spring study. Each semester comprises a minimum of 36 units. Students graduate in May.

Core Curriculum

All MCDS students must complete 144 units of graduate study which satisfy the following curriculum:

• Five (5) MCDS Core Courses (63 units)
• Three courses (3) from one area of concentration curriculum (36 units)
• Three (3) MCDS Capstone courses (11-635, 11-634 and 11-632) (36 units)
• One (1) Electives: any graduate level course 600 and above in the School of Computer Science (12 units)

Area of Concentration

• During the first two semesters in the program, all students take a set of five (5) required core courses: 11-637 Fundamentals of Computational Data Science, 15-619 Cloud Computing, 10-601 Machine Learning, 05-839 Interactive Data Science, and 11-631 Data Science Seminar.
• By the end of the first semester, all students must select at least one area of concentration — Systems, Analytics, or Human-Centered Data Science — which governs the courses taken after the first semester.
• To maximize your chances of success in the program, you should consider which concentration area(s) you are best prepared for, based on your educational background, work experience, and  areas of interest as described in your Statement of Purpose.
• You are strongly encouraged to review the detailed curriculum requirements for each concentration area, in order to determine the best fit given your preparation and background.

For a complete overview of the MCDS requirements read the  MCDS Handbook .

To earn an MCDS degree, students must pass courses in the core curriculum, the MCDS seminar, a concentration area, and electives. Students must also complete a capstone project in which they work on a research project at CMU or on an industry-sponsored project.

In total, students must complete 144 eligible units of study, including eight 12-unit courses, two 12-unit seminar courses, and one 24-unit capstone course. Students must choose at minimum five core courses. The remainder of the 12-unit courses with course numbers 600 or greater can be electives chosen from the SCS course catalog. Any additional non-prerequisite units taken beyond the 144 units are also considered electives.

Students who plan to select the Systems concentration may wish to enroll in 15-513 “Introduction to Computing Systems” during the summer session preceding their enrollment in the program; this course is a prerequisite for many advanced Systems courses, so it should be completed during Summer if you wish to enroll in advanced Systems courses in the Fall.

Click here   to see the MCDS Course Map.

Some example courses of study are included below.

Example 1: Analytics Major, 16 Months

Example 2: Systems Major, 16 Months

Example 3: Human-Centered Data Science Major, 16 Months

Carnegie Mellon's School of Computer Science has a centralized  online application process . Applications and all supporting documentation for fall admission to any of the LTI's graduate programs must be received by the application deadline. Incomplete applications will not be considered.  The application period for Fall 2024 is now closed. Information about the Fall 2025 admissions cycle will be available in summer 2024.

Application Deadlines

Fee Waivers

Fee waivers may be available in cases of financial hardship, or for participants in select "pipeline" programs. For more information, please refer to the  School of Computer Science Fee Waiver page .

The School of Computer Science requires the following for all applications:

• A GPA of 3.0 or higher.
• GRE scores: These must be less than five years old. Our Institution Code is 2074; Department Code is 0402. (This requirement is waived for CMU undergrads.)
• The GRE At Home test is accepted but we prefer you take the GRE at a test center if possible.
• Unofficial transcripts from each university you have attended, regardless of whether you received your degree there.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation
• A short (1-3 minutes) video of yourself. Tell us about you and why you are interested in the MCDS program. This is not a required part of the application process, but it is STRONGLY suggested.  
• Proof of English Language Proficiency

Proof of English Language Proficiency: If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo. We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored.

We do not issue waivers for non-native speakers of English. In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university. We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States. No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Applicants applying to MCDS are required to submit scores from an English proficiency exam taken within the last two years. Scores taken before Sept. 1, 2021, will not be accepted regardless of whether you have previously studied in the U.S. For more information about their English proficiency score policies, visit the  MCDS  admission website.  Successful applicants will have a minimum TOEFL score of 100, IELTS score of 7.5, or DuoLingo score of 120. Our Institution Code is 4256; the Department Code is 78. Additional details about English proficiency requirements are provided on the  FAQ  page. 

Applications which do not meet  all  of these requirements by the application deadline (see above) will not be reviewed.

For more details on these requirements, please see the  SCS Master's Admissions page.

In addition to the SCS guidelines, the LTI requires:

• Any outside funding you are receiving must be accompanied by an official award letter.

No incomplete applications will be eligible for consideration.

For specific application/admissions questions, please contact  Jennifer Lucas  or Caitlin Korpus .

Program Contact

For more information about the MCDS program, contact Jennifer Lucas or Caitlin Korpus

Jennifer Lucas

Caitlin korpus, online graduate certificate program, program handbook.

Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

At a Glance

Start Date May 2024

Application Deadlines Rolling Admissions

We are still accepting applications for a limited number of remaining spots to start in Summer 2024. Apply today to secure your space in the program.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.

PhD student James Madeley awarded Internet Society fellowship

Computer Science PhD student James Madeley will spend the next six months working on an exciting project with the Internet Society after being awarded a fellowship.

Computer Science PhD student James Madeley recently celebrated success after being awarded the Pulse Research Fellowship from the Internet Society.

James, who studied his bachelor's degree in Computer Science and is currently in the third year of his doctoral degree, both at Loughborough University, will be working with the Internet Society Pulse Research team to develop his project titled: LocalViz: Measuring and Visualizing Internet Traffic Locality.

Talking about the announcement, James said: "I was thrilled to find out that I was awarded the fellowship. The Internet Society does a lot of great work, so being able to take part in research working towards their 50/50 Vision is an exciting prospect."

James' project will focus on measuring how much internet traffic remains in the country it originates from. He will aim to measure how much traffic a country produces, where this traffic goes, and how the results can be clearly communicated. James explains that this is key because local traffic is cheaper, faster, and leads to improved resilience.

He added: "For example, if you are accessing government or news websites from your own country, it makes sense to go direct to the data, rather than sending the data across expensive international links to end up back in the country you started from. This is easily done in countries with lots of infrastructure but can be very challenging in countries that are less developed. Understanding the current state of traffic is a key step towards increasing locality for countries that need it most."

James said he is excited to be working on a project that aligns with Loughborough's strategic theme of creating 'vibrant and inclusive communities' and its overall strategy of ' Creating Better Futures. Together. '

James explains: "My project fits perfectly within this strategy, aiming to deliver meaningful and impactful research that can go on to provide global benefits. Understanding the current state of traffic on the Internet is crucial to shaping how it develops and, although a six-month fellowship is only a small part of the bigger picture, Loughborough is a fitting place to start such a journey."

James hopes to publish at least one academic paper during his fellowship and will provide updates through blog posts as the project progresses, as well as creating measurement tools and a visualisation platform that can be used by the Internet Society for their longer-term research goals.

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PhD student receives fellowship from Apple Scholars program

Nataliya Nechyporenko, a computer science Ph.D. student, has received a PhD fellowship in AI and Machine Learning (AIML) through the Apple Scholars program . The program was created by Apple to recognize the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. 

The fellowship provides Nechyporenko support for her research and academic travel for two years, internship opportunities and a two-year mentorship with an Apple researcher. 

Let's learn more about Nechyporenko's research aims and her perspective on the future of robotics research: 

What research do you hope to accomplish through this fellowship?

Think about how you might manually feel around an object to understand its shape, weight, and texture. Or if something is in your way, you'd just push it aside without overthinking it. If you drop something, you'll persistently keep trying to pick it up from different angles until you get it. As you're doing these everyday tasks, you're constantly building up an intuitive sense of your surroundings through trial-and-error. That's the kind of resourceful, flexible, multi-sensory approach I want robots to have when manipulating things – rather than just blindly following a fixed routine. 

The goal is for robotic arms to move and behave with that same kind of curious, improvisational, problem-solving spirit we take for granted as humans. As an Apple AIML scholar, I hope to gain insights into this problem with the help of a fresh network of mentors and collaborators.  

Is this an extension of work you are already doing in your lab? If so, how?

Driven to establish contact-rich planning as a dominant feature in robotics, I focused the first two years of my PhD on analyzing the methods used by state-of-the-art planners and solving the shortcomings leading to the lack of physical robot interaction. 

I have started to extend this work by integrating the empirical formulation of machine learning with model-based algorithmic approaches. I believe this is the path to making robots more adaptable to chaotic human environments. I will continue this work as an Apple scholar. 

What do you think of the current hype around AI and ML? What do you wish people understood about this research area?

The AI and machine learning hype trains have been barreling full steam ahead lately. But robotics? That's an entirely different beast that doesn't follow the overnight disruption narratives. It's a synergy of achievements in areas like materials, manufacturing, sensing, controls theory, and others aligning to reshape the physical world. 

The robotics future will reshape industries and labor concepts, but it will be catalyzed through the patient advancement of many disciplines.

How did you come to study at CU Boulder?

I spent a couple years in the trenches, getting my hands dirty actually building and deploying robots in industry. But after a while, I got this craving -- like there was so much more potential waiting to be unlocked if I could really dive into the deep scientific questions around robotics. That's why I decided to take the plunge back into academia.

What is one of your plans or hopes for the future, either professionally or personally?

I hope to be an expert, a leader, a thinker and a builder. Outside of research endeavors, I aim to be a leader and educator for the robotics and the AI community. Previously, I’ve led volunteering activities, mentored students, and co-organized events that foster discussions around AI. I hope to continue to do so in the future at a larger scale. 

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Nataliya Nechyporenko

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Graduate Diploma in Computer Science

Change is the only constant in the world of computing. If you're a computing professional looking to upgrade, update or deepen your knowledge of rapidly evolving technologies – this program is for you.

The Graduate Diploma of Computer Science is designed for applicants with a bachelor's degree in computer science or information technology (or a related field). There are other ways to meet admission requirements – see Entry Requirements.

You can focus on one or several areas of interest when choosing from the range of computer science courses, including an advanced research project at masters level.

You will be taught by a mix of internationally renowned lecturers, industry professionals and leading researchers. Our learning spaces are some of the most innovative in the world, allowing students to share ideas, help each other and socialise.

Join a growing industry

• Demand for technology workers will grow by 100,000 between 2018 and 2024 (ACS Australia’s Digital Pulse 2019, Deloitte)
• Computer science research jobs will grow 19% by 2026 (Bureau of Labor Statistics)

Program highlights

• Complete your choice of courses that cover topics from advanced computer science, software engineering, information systems, communication systems, interaction design, research and more. In total, there are nearly 50 courses to choose from.
• Undertake a research project that addresses a specific topic or problem from the broad fields of electrical, computer systems or software engineering.
• Benefit from a program that offers a flexible study plan. Tailor your studies to suit your interests, your industry, or your career goals.

1 in Queensland for computer science and information systems

QS World University Rankings 2024

1 in Queensland for mathematics

How you'll learn

Your learning experiences are designed to best suit the learning outcomes of the courses you choose.

• Research experience
• Laboratory work

What you'll study

At UQ, degrees are called 'programs' and subjects are called 'courses'. Here's a sample of the courses you could study in this program:

• Algorithms and Data Structures
• Artificial Intelligence
• Advanced Topics in Security
• Machine Learning

See courses and program structure

Career possibilities

Postgraduate study can take you anywhere. Here are some of the careers you could be on your way to:

• Business analyst
• Data scientist
• Digital analyst
• Market analyst
• Big data architect
• Data migration specialist
• Social media data strategist
• Information architect
• Cloud architect
• IT support officer

Graduate salary

Computing & information systems (postgraduate)

compared.edu.au

Next steps after graduation

The Graduate Diploma in Computer Science equips students with advanced-level knowledge and skills in relevant areas, such as information systems, software engineering, distributed systems, networks, research and security.

Graduates work across a variety of fields and professions.

Some graduates choose to study higher degrees and go on to research positions at universities or other major research organisations. Other graduates work in industry – as analysts, engineers, administrators, developers, project managers and in specialist roles – with an increasing number of graduates employed in banking, finance and insurance.

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Entry requirements

To be eligible for entry, you'll need:

• a bachelor's degree (or equivalent) in computer science or software engineering, or
• a bachelor's degree (or equivalent) which includes all of the relevant discipline content (see below), or
• to have completed post-secondary studies and 2 years full-time equivalent relevant work experience (see below). Applications based on post-secondary study and/or work experience will be individually assessed.

Relevant disciplines for previous qualifications

If your bachelor's degree was not awarded in computer science or software engineering, you must have successfully completed all of the following discipline content in your tertiary studies:

• data structures and algorithms
• at least 2 programming courses
• discrete mathematics or algebra
• at least 3 from the following:
• computer organisation or computer architecture or computer systems
• computer networks or communication networks
• operating systems
• databases or information systems
• probability and statistics

Relevant work experience

Relevant work experience includes professional experience in software development or engineering, cyber security analysis or architecture, data engineering or science, machine learning, computer networks or statistical analysis that involves programming experience and work experience in at least two of the following areas:

• probability and statistics 

This will need to be supported with evidence. 

Evidence of relevant work experience should include a letter from your employer (and/or previous employers) clearly stating the following:

• That you work (or worked) within the specified organisation
• The nature of your work, detailing any relevant duties and responsibilities to the entry criteria above
• The length of time you were in your role/s (i.e demonstrating minimum length for entry) and whether this was full-time, part-time, or casual
• Any further bespoke conditions listed by the entry criteria

Letters will typically be expected to be presented on company letterhead and signed by a manager or HR representative. A CV or resume is not a sufficient document on its own, and must be accompanied by a supporting letter as described above.

All applications based on work experience are subject to an individual assessment.

Entry into a program through work experience does not necessarily provide a pathway into further study in a Masters.

GPA equivalent

Select where you studied and your qualification to see the GPA equivalent you need to be considered for this program.

Use the GPA equivalent as a guide. When you apply, we’ll calculate your GPA using the UQ grading scale. Any failing grades will be included. Entry requirements are subject to change.

Equivalent subjects

Related programs.

Depending on your previous qualifications and current goals, you might want to consider one of these related programs:

• Master of Computer Science
• Master of Computer Science (Management)
• Graduate Certificate in Computer Science

English language requirements

IELTS overall 6.5; reading 6; writing 6; speaking 6; listening 6. For other English Language Proficiency Tests and Scores approved for UQ

TOEFL iBT (including Paper Edition) - Overall 87, listening 19, reading 19, writing 21 and speaking 19.

PTE Academic - Overall Score of 64 and 60 in all sub bands.

BE - A minimum overall grade of 4 plus a minimum grade of C in all macro skills.

CES - Overall 176 and 169 in all sub bands.

OET is not accepted.

There are other ways to meet the English language requirements. For some programs, additional conditions apply.

Learn how to meet the English language requirements

Student visas

International students who are accepted into full-time study in the Graduate Diploma in Computer Science are eligible to apply for an Australian student visa (subclass 500).

There are a number of requirements you must satisfy before a visa is granted, including the Genuine Student (GS) requirement.

Learn more about student visas

Fees and Scholarships

Indicative annual fee.

Approximate yearly cost of tuition (16 units). Your fees will vary according to your selected courses and study load. Fees are reviewed each year and may increase.

Fee information for 2025 is not yet available. Fee information displayed is for 2024.

Learn more about postgraduate fees

Approximate yearly cost of full-time tuition (16 units). Your fees will vary according to your study load. Fees are reviewed each year and may increase.

AUD $53,760

Government assistance, financial aid.

As an international student, you might be eligible for financial aid – either from your home country, or from the Australian Government.

Learn more about financial aid

Domestic places in the Graduate Diploma in Computer Science are Commonwealth Supported. This means the cost of your education is shared between you and the Australian Government.

Instead of tuition fees, Commonwealth Supported students pay what are called student contribution amounts.

HECS-HELP is an Australian Government loan scheme to assist eligible students with the cost of their student contribution amounts.

Learn more about HECS-HELP

Centrelink support

The Australian Government offers a number of income-support payments to eligible Australian university students.

Learn about Centrelink payments for students

Scholarships

You may be eligible for more than 100 scholarships, including:

Applying online

All international applications should be submitted to UQ. If you prefer, you can use an  approved UQ agent in your country .

The program code for the Graduate Diploma in Computer Science is  5520 .

Find out more about applying for postgraduate coursework study

All domestic applications should be submitted to UQ.

The program code for the Graduate Diploma in Computer Science is 5520 .

Important dates

The closing date for this program is:

• To commence study in semester 2 - May 31 of the year of commencement.
• To commence study in semester 1 - November 30 of the previous year.

To learn more about UQ dates, including semester start dates, view the Academic Calendar .

• To commence study in Semester 1 - January 31 of the year of commencement.
• To commence study in Semester 2 - June 30 of the year of commencement.

Aboriginal and Torres Strait Islander applicants

For support with applying – or if you have any questions about university life – get in touch with our Aboriginal and Torres Strait Islander Studies Unit.

Contact the ATSIS Unit

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