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Postdoctoral Research Associate in Information Theory and Statistics
2 weeks ago
We would like to advertise one position for a Post-Doctoral Research Associate to work in topics at the intersection of information theory and statistics. This position will be funded by the EPSRC AI Hub on Information Theory for Distributed Artificial Intelligence (INFORMED-AI). INFORMED-AI is a joint programme run by the University of Bristol, the University of Cambridge, the University of Durham, and Imperial College London. The successful candidate will be based in Cambridge, primarily hosted by Varun Jog and co-supervised by Po-Ling Loh (Cambridge) and Sidharth Jaggi (Bristol).
The PDRA's main project will involve investigating several theoretical questions concerning distributed testing and estimation, particularly in streaming settings where agents have limited memory. They will study tradeoffs between memory, accuracy, and sample complexity of fundamental questions in hypothesis testing. In addition, the PDRA may be responsible for developing and conducting collaborative research projects as part of the overall work of the INFORMED-AI programme.
This is an exceptional opportunity to conduct ambitious research at the forefront of mathematics, statistics, information theory, and machine learning. There are generous funds available for conference attendance, travel, computer equipment, training, and career development.
The vision and ambition of INFORMED-AI is to develop the theoretical foundations of artificial intelligence, specifically in the area of collective intelligence, addressing aims such as 1) trustworthy collective intelligence, 2) connectivity and resilience, and 3) heterogeneous distributed artificial intelligence.
The four-university team which the successful candidate will join combines leading expertise in information theory, theoretical statistics, applied probability, optimization, robustness, privacy, machine learning, game theory, artificial intelligence, and robotics. Interaction with industrial partners will be encouraged. The ideal candidate will also help serve as a bridge between the information theory groups at Cambridge and Bristol. In particular, while based in Cambridge, the PDRA would be expected to make regular visits to Bristol and engage in the academic communities at both Cambridge and Bristol, participating regularly in IT/statistics/ML seminars, attending and presenting in reading groups, and helping co-supervise PhD students.
Applicants must have (or be about to receive) a PhD degree in mathematics, statistics, engineering, or computer science. The ideal candidates will be experienced in one or more of the following areas: classical or quantum information theory, mathematical statistics, machine learning, and optimization.
Limit of tenure: 2 years, with possible extension as funds permit.
Start date: 1 July 2026 or by mutual agreement.
Please indicate the contact details of two academic referees on the online application form and upload a full curriculum vitae and a research statement (not to exceed three pages). Please ensure that at least one of your referees is contactable at any time during the selection process and is made aware that they will be contacted by the Mathematics HR Administrator to request that they upload a reference for you to our Web Recruitment System, and please encourage them to do so promptly.
Closing date for applications is 1 December 2025.
Interviews will take place as soon as possible following the closing date.
If you have any questions about this vacancy or the application process, please contact
Please quote reference LF47619 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
Key informationDepartment/location
Department of Pure Mathematics and Mathematical Statistics
Salary£37,694-£46,049
ReferenceLF47619
CategoryResearch
Date published15 October 2025
Closing date1 December 2025
