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Research Assistant in Information Theory x 3
Up to three Research Assistant positions to work on Shannon Theory at the University of Cambridge.

Applications are invited for up to three Research Assistant positions to work on the ERC Advanced Grant on "Scaling and Concentration Laws in Information Theory" led by Professor Albert Guillén i Fàbregas. The project studies the fundamental limits of channels and sources where the optimal number of messages scales sub-exponentially with the length, concentration laws for random coding, sub-optimal decoding, the design of optimal codes, as well as related statistical inference problems.

Successful candidates will have a genuine interest in Shannon Theory and will have a background in information theory, statistics, communications theory and/or channel coding, and will conduct research related to the project. A background in optimization and asymptotic methods in probability and statistics is desirable. The specific area of research will be determined in consultation with the PI, considering the skills and interests of the successful candidates. Successful candidates will work closely with the PI (Professor Albert Guillén i Fàbregas) and actively participate in the activities of the PI's research group. The project will be run in collaboration with the Universitat Politècnica de Catalunya, Barcelona, and short research stays in Barcelona will be possible.

Key responsibilities and duties include undertaking an independent program of research and assisting with ongoing research lines, developing numerical experiments, writing up results for publication, and presenting work to colleagues at conferences or seminars, both internally and externally. Funding for international conference travel will be available. The successful candidates may also be asked to assist in organising various seminars and study groups.

During the period of funding for this role, successful candidates may be eligible to complete their PhD studies.

Salary Ranges: £32,546 - £35,116

More details and applications: https://www.jobs.cam.ac.uk/job/51388/