Research Associate in LLM and Multimodal Reinforcement Learning

Fixed term contract - 15 months.

Full time - 35 hours per week.

The School of Informatics, University of Edinburgh invites applications for a Research Associate in Deep Learning, Machine Learning Methods and Reinforcement Learning, to work with Prof Amos Storkey, Dr Peter Bell, and Dr Stefano Albrecht.

The researcher will work on multi-modal data including text and visual information to help with data efficient reinforcement learning and offline reinforcement learning. They will be  part of the new Edinburgh Laboratory for Integrated Artificial Intelligence, the Bayesian and Neural Systems Group and the  Autonomous Agents Research Group.

The opportunity:

The School of Informatics is one of the largest research centres in Computer Science in Europe, and it has been ranked #1 in the UK in terms of research power by a large margin. Informatics, Edinburgh is world renowned in Machine Learning and Reinforcement Learning, publishing in all the top venues in these fields. We are offering an exciting opportunity to work in an interdisciplinary, collaborative, friendly, and supportive environment, integrating different sub-fields of within Artificial Intelligence. 

Reinforcement learning (RL) algorithms aim to train a decision policy for an agent to achieve a specified task in an environment. The agent’s policy is trained by choosing actions which maximise the cumulative rewards received by the agent from its environment. With the introduction of deep learning into RL, “deep RL” algorithms have achieved unprecedented scalability, enabling the solution of complex decision tasks, such as autonomous driving [1] and beating human champions in games such as Go [2]. However, a current limitation of deep RL is that the training process typically requires orders of many millions of environment interactions in the precise environment (i.e. training data), which is infeasible in real-world applications where obtaining samples poses a significant bottleneck. Good transferability between different related environments is also still not generally achievable. Thus, developing approaches to improve the sample efficiency of deep RL algorithms, i.e. learning good policies with minimal data or from information learnt elsewhere, is a high-priority research topic.

This post is full-time (35 hours per week) and fixed term for 15 months.

Your skills and attributes for success:

• PhD (or near completion) or equivalent research experience in artificial intelligence, machine learning methods, machine learning in language processing, reinforcement learning or a related discipline (Desirable).
• Experience and evidence of effective independent research work within a research team, and contribution to the team effort (Desirable).
• Demonstrated quality of research performance, as evidenced by high-quality publications in top-tier machine learning / computer vision, RL or related venues (e.g., ICML, NeurIPS, ICLR, AISTATS, UAI, AAAI, ACL, EMNLP, ICCV, ECCV, CVPR, and relevant journals (IEEE PAMI, JMLR among others).
• Strong programming skills; experience with Python and deep learning libraries (e.g., PyTorch or TensorFlow).

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