Research Associate or Research Fellow in Machine Learning; Four
Listed on 2026-06-04
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Research/Development
Research Scientist, Data Scientist
Salary: x3 £ 36,024 to £44,263 or 1x £45,585 to £56,021 per annum, depending on relevant experience
Faculty/Organisational Unit:
Science and Engineering
Location:
Oxford Road
Employment type:
Fixed Term
Division/Team:
Department of Computer Science
Hours Per Week:
Full Time
Closing date (DD/MM/YYYY): 27/06/2024
Contract Duration:
For 24 months
School/Directorate:
School of Engineering
Applicants are invited for the posts of Postdoctoral Research Associate or Research Fellow in Machine Learning to work on one of several projects affiliated to the Centre for AI Fundamentals at the University of Manchester.
You will join a team of probabilistic modellers and machine learning researchers developing new collaborative AI principles and methods. This is an exciting topic which inspires new problems in fundamental ML work, and allows attacking new applications which make a difference, for instance in scientific research. Different team members have different expertise, and by working together are able to address more novel problems.
Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy‑preserving learning, reinforcement learning, inverse reinforcement learning, computational rationality and user modelling, and simulator‑based inference.
(One Research Associate post available)
The post-holder will work on a project from one of the above programmes.
QualificationsA PhD or equivalent in a relevant discipline is required. You should have excellent organisational skills and the ability to work well both in a team and using your own initiative. Experience in research methods and techniques to work within established research programmes is essential.
Equal Opportunities StatementAs an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
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