Research Associate in Bayesian Deep Learning for AI Safety

Department of Computer Science, Wolfson Building, Parks Road, Oxford.
Research Associate in Bayesian Deep Learning for AI Safety
Fixed-term for up to 24 months
Grade 7: £32,236 - £39,609 p.a.

The Artificial Intelligence and Machine Learning group at the Department of Computer Science has a new opening for a Research Associate in Bayesian deep learning for AI safety, working together with Professor Yarin Gal in an industrial partnership with Accenture.  Conducting original research in this area, you will develop fundamental tools at the core of Bayesian deep learning, in the context of real-world AI safety problems.

The aim of this project is to develop principled but practical AI safety methods in Bayesian deep learning which are practical enough to 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) AI safety, as well as ii) Bayesian machine learning, and iii) deep learning. The project thus requires familiarity with all fields above, and extensive experience in at least two of the fields. The project will involve both theoretical work as well as extensive empirical analysis on challenging tasks. The researcher will be employed by Oxford but with a secondment to the Turing Institute in London.

The primary selection criteria are a doctoral degree (or close to completion) 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 peer-reviewed work in recognised venues in machine learning on the specific topics of Bayesian deep learning/AI safety, and strong mathematical skills in probability and statistics.  Good knowledge of the current state-of-the-art in AI safety and Bayesian deep learning, and experience supervising PhD students and managing projects is highly desirable.

For further details and to apply please visit:
https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=139152  

The closing date for applications is 12 noon on 25 July 2019.

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