PhD studentship in "Machine Learning Theory & Economics"

Deadline: 5 January 2024 (or until the position is filled)

One fully funded, full-time PhD position to work with Dr Fengxiang He at the Artificial Intelligence and its Applications Institute (AIAI), School of Informatics, University of Edinburgh on a project titled “Machine Learning Theory & Economics”.

The aim of this project could be in deep learning theory, privacy in machine learning, theory of decentralised learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics, such as auction, voting, resource allocation, and decentralised finance.

Candidate’s profile

  • A Bachelor’s Hons degree (at class 2.1 or above, or international equivalent) and/or Master’s degree in a relevant subject (mathematics, statistics, economics, or related subject)
  • A strong mathematical background, with an emphasis on analysis, algebra, geometry, differential equation, probability, and statistics. Recipients of mathematics competition medals are highly desirable
  • Proficiency in English (both oral and written)
  • Relevant research experiences in machine learning, statistics, economics, etc. are desirable but not necessary
  • Programming skills in Python, PyTorch, TensorFlow, etc. are a plus but not necessary

Studentship and eligibility

The School funded studentship starting in the academic year 2023/24 covers:

  • Full time PhD tuition fees for a student with a Home fee status (£4,712 per annum) and/or overseas fee status (£29,700 per annum)
  • A tax-free stipend of £18,622 per year for 3.5 years
  • Additional programme costs of £1,000 per year

Application information

Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: AIAI: Foundations and Applications of Artificial Intelligence, Automated Reasoning, Agents, Data Intensive Research.

Applicants should select a start date to start their application, as follows:

  • 1 May 2024 (Home applicants only)
  • 1 September 2024 (Home and International applicants)

Applicants should state “Machine Learning Theory & Economics” and the research supervisor (Dr Fengxiang He) in their application and Research Proposal document.

Complete applications submitted by 5 January 2023 will receive full consideration; after that date applications will be considered until the position is filled. The anticipated start date is 1 May 2024, 1 September 2024, or later depending on immigration requirements of the successful applicant.

Applicants must submit:

  • All degree transcripts and certificates (and certified translations if applicable)
  • Evidence of English Language capability (where applicable)
  • A short research proposal (max 2 pages)
  • A full CV and cover letter describing your background, suitability for the PhD, and research interests (max 2 pages)
  • Two references (note that it the applicant’s responsibility to ensure reference letters are received before the deadline)

Only complete applications (i.e., those that are not missing the above documentation) will progress forward to Academic Selectors for further consideration.


Applicants are highly encouraged to contact Dr Fengxiang He at to discuss their cases before submissions.


The School of Informatics is one of the largest in Europe and currently the top Informatics institute in the UK for research power, with 40% of its research outputs considered world-leading (top grade), and almost 50% considered top grade for societal impact. The University of Edinburgh is constantly ranked among the world’s top universities and is a highly international environment with several centres of excellence.

The School of Informatics is exceptionally strong in the area of AI, hosting one of the largest groups for AI in the world. The successful applicant will be part of the Trustworthy AI & Economics group and will have the opportunity to interact with the other members of the group and more widely within the School of Informatics.


Dr Fengxiang He is Lecturer at Artificial Intelligence and its Application Institute, School of Informatics, University of Edinburgh. He received BSc in statistics from University of Science and Technology of China, MPhil and PhD from the University of Sydney. His research interest is trustworthy AI, with emphasis on deep learning theory and explainability, theory of decentralised learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics and finance. He is a member of IEEE's Global Initiative on XR Ethics, AI/ML Terminology and Data Formats Working Group, Decentralized Metaverse Initiative, and Ethical Assurance of Data-Driven Technologies for Mental Healthcare. He is an Area Chair of UAI, AISTATS, and ACML. Please visit Fengxiang He's homepage for more information.



Dr Fengxiang He