Postdoc Research Associate in Multi-agent Reinforcement Learning and Foundation Models

Job id: 084006. Salary: £43,205 per annum, including London Weighting Allowance.

Posted: 06 February 2024. Closing date: 12 March 2024.

Business unit: Natural, Mathematical & Engineering Sci. Department: Informatics.

Contact details:Yali Du. [email protected]

Location: Bush House, Centre Block, Strand Campus. Category: Research.

Job description

The department of Informatics is looking to appoint a motivated candidate to fill a postdoctoral research associate position in Multi-agent Reinforcement Learning and Foundation Models. This position will be hosted and located in the Cooperative AI Lab (https://coopai.kcl.ac.uk/ ) headed by Dr. Yali Du in the Department of Informatics, King’s College London, United Kingdom. The researcher will be supervised jointly by Dr. Yali Du (King’s College London) and Dr. Biwei Huang (University of California San Diego).

The candidate will be a conscientious, innovative scientist who has completed a Ph.D. (or equivalent) in computer science, engineering (including electrical/electronic engineering), or a similar field. Candidates with strong backgrounds in reinforcement learning, multi-agent learning, language model agents and causal learning are encouraged to apply.  Inter-disciplinary research experience and project management experience are preferred. You will join a team, with an opportunity to collaborate with researchers at Google DeepMind, University of Cambridge and the Turing Institute.

At King’s, we are part of the Distributed Artificial Intelligence group at the Department of Informatics, a vibrant, open and collaborative research and learning environment.

This post will be offered on an initial one-year contract. The post is due to start in the Spring of 2024 or as early as possible thereafter.

This is a full-time  post – 100% full time equivalent

Key responsibilities 

• Develop multi-agent simulation algorithms to help advocate cooperation behaviors in multi-agent games. Also, using causal-related approach to model the multi-agent system.
• The Research Fellow will conduct theoretical and experimental research, bridging the areas among reinforcement learning and reality cooperation problems, to formulate and study a wide range of reality problems.
• Help supervise the research team and write project reports.
• Publish high-quality research papers in well-recognized peer-reviewed journals.
• Present research findings at national and international conferences.
• Provide valuable insights into the wider research program.
• Working and collaborate deeply with the fellow research teams
• Communicate research outcomes to a non-specialist audience.

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.

Skills, knowledge, and experience 

• Evidence of a scientifically rigorous approach
• Good English oral and written communication skills
• Strong analytical, creative, and problem-solving skills
• A methodical and accurate approach to work with attention to detail
• Ability to work without direct supervision
• Ability to assimilate and act on advice from the supervisors and other team members

Essential criteria

Qualifications, experience and knowledge

1.       BSc in Computer Science, Electronic Engineering, or related area.

2.       PhD awarded in a relevant topic of Computer Science, Electronic Engineering or related area (or near completion).

Skills andabilities

3.       Ability to demonstrate a record of high-quality peer-reviewed top AI or conference or top journal publications.

4.       Ability to carry out innovative research in machine learning, reinforcement learning and multi-agent reinforcement learning.

5.       Ability to use Python (or any other programming languages) with strong coding skills.

6.       Good interpersonal / team working skills. 

Desirable criteria

1.       Knowledge of multi-agent reinforcement learning.

2.       Knowledge on causal inference. 

3.       Desire to learn new knowledge and methods to solve interesting and important research problems.

To apply for this position, candidates need to submit their CV with full publications, and a cover letter explaining their experience and motivation for applying to this position.