Research Assistant in Bayesian Deep Learning

University of Oxford

The Oxford Applied and Theoretical Machine Learning group at the Department of Computer Science has a new opening for a Researcher in Bayesian deep learning, working together with Professor Yarin Gal. Conducting original research, you will develop fundamental tools at the core of Bayesian deep learning in the context of real-world AI problems.

The aim of this project is to develop principled but practical safe AI methods in Bayesian deep learning which are practical, ie could be used in real systems. This requires coping with challenges such as intractable probabilistic inference and robustness. The focus of the project is on methods for i) Bayesian machine learning, and ii) deep learning. The project thus requires familiarity with the fields above, and experience in at least one of the fields. The project will involve both theoretical work as well as empirical analysis on challenging tasks.

The primary selection criteria are an MSc degree in Computer Science or related discipline, together with related experience, a documented track record of the ability to conduct and complete research projects, as witnessed by published work in machine learning on the specific topics of Bayesian deep learning, and strong mathematical skills in probability and statistics.  Good knowledge of the current state-of-the-art in safe AI and Bayesian deep learning, and experience managing projects is highly desirable.

The closing date for applications is 12 noon on 11th December 2023. Interviews are expected to be held in December. 

We are a Stonewall Silver Employer, Living Wage, holding an Athena Swan Bronze Award, HR excellence in Research and Race Equality Charter Bronze Award.

Our staff and students come from all over the world and we proudly promote a friendly and inclusive culture. Diversity is positively encouraged, through diversity groups and champions, for example , as well as a number of family-friendly policies, such as the right to apply for flexible working and support for staff returning from periods of extended absence, for example shared parental leave. 

Demonstrating a commitment to provide equality of opportunity. We would particularly welcome applications from women and black and minority ethnic applicants who are currently under-represented within the Computer Science Department. All applicants will be judged on merit, according to the selection criteria.

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