Fully funded, full-time PhD position to work in the field of Trustworthy AI with Dr Fengxiang He from the Trustworthy AI & Economics group (TAIEG) at Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh.
Visiting Scholar/Intern positions are also possible. Please contact Dr He if you are interested.
Candidate’s profile
• A good Bachelor’s degree (First Class Honours 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.
Contact
Applicants are highly encouraged to contact Dr Fengxiang He at F.He@ed.ac.uk to discuss your case.
Environment & supervisor
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 one of the largest in Europe and currently the top Informatics institute in the UK for research power. The School is exceptionally strong in the area of AI and Theoretical Computer Science, hosting one of the largest groups for AI and Foundations of Computer Science 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. His research interest is trustworthy AI, including deep learning theory, privacy-preserving machine learning, decentralized learning, algorithmic game theory, etc., and their applications in finance and economics. He is an Area Chair of UAI, AISTATS, and ACML. Please visit https://fengxianghe.github.io/ for more information.
