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Research Associate in Reinforcement Learning

Posted: 23rd August 2018

Department of Computer Science, Wolfson Building, Parks Road, Oxford.

Research Associate in Reinforcement Learning

Fixed-term until 30 September 2020 (starting as soon as possible)  

Grade 7: £31,604 - £38,833 p.a.

A two year, full-time postdoctoral position is available for a researcher in the area of reinforcement learning. This is a European Research Council-funded project, working with Professor Shimon Whiteson.  The goal of the project is to develop a new class of reinforcement learning methods that overcome fundamental obstacles to the efficient optimisation of control policies for autonomous agents. Creating agents that are effective in diverse settings is a key goal of artificial intelligence with large potential implications in robotics, e-commerce, information retrieval, traffic control, etc.

The post holder will be expected to collaborate in the preparation of research papers, development and analysis of new algorithms, present papers at conferences, act as a source of information and advice to other members of the group on scientific procedures and experimental techniques, which will focus on machine learning, reinforcement learning, and deep learning.  

The post holder will require a doctoral degree (or be close to completion) in Computer Science or a related area, together with a documented track record of published, peer-reviewed research in machine learning (or a related area) and possess strong mathematical skills in probability and statistics.

The closing date for applications is 12 noon on 24 September 2018. 

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 maternity leave.

For further details and to apply please visit: