Research Associate in Machine Learning: Planning and Reinforcement Learning for Long Horizons

Research Associate in Machine Learning: Planning and Reinforcement Learning for Long Horizons

Grade UE07: £40,247 to £47,874 per annum

School of Informatics / CSE

Full time: 35 hours per week.

Fixed Term: for two years.

 

The School of Informatics at the University of Edinburgh is inviting applications for a Research Associate in Machine Learning and Artificial Intelligence with a particular focus on method development for Planning in the context of diverse complex long-horizon settings. Two particular settings include event based planning (e.g. planning a trip) and forecast-based planning (e.g. allocating resources).

The Opportunity:

Real world planning is a complex process that needs to account for many stages that might be needed to execute a plan, significant uncertainties in the states that result from actions in a plan, and diverse sources of knowledge that contribute information relevant for plan formulation. Plan’s are made and changed dynamically. Furthermore it is not usually possible to interact with the environment extensively enough to learn a value function associated with state-actions.

Most reinforcement learning (RL) systems break in these circumstances. Yet humans are very able to make such plans. This project will attempt to move to these more realistic planning scenarios, by integrating systemic knowledge encoded in knowledge graphs, large language models for re-representing and formulating that knowledge, and world-modelling through forecasting to understand the scenario options. This will then prove a model-based environment as well as surrogate reward functions. These can be used via RL-like methodology to enable us to learn appropriate planners for particular scenarios. We will look at trip planning and load allocation as two example applications. 

This project is funded by Huawei UK is led by Amos Storkey along with Jeff Pan. The successful candidate will be based in the Informatics Forum, in central Edinburgh, and will work with colleagues in the Bayesian and Neural Systems Group and in collaboration with colleagues in Huawei Technologies Research & Development (UK) in Edinburgh.

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, 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. 

We welcome both local (UK-resident) and international applicants. This position will include funding for international travel – e.g., for attending conferences, visiting research collaborators, and disseminating research findings. Furthermore, the researcher will have access to the computing infrastructure and office spaces available within Informatics and the research groups.

We are strongly committed to offering everyone an inclusive and non-discriminating working environment. We warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from under-represented groups in the field.

Your skills and attributes for success

• PhD (or near completion) or equivalent research experience in artificial intelligence, machine learning methods, machine learning systems or a very related discipline.
• Experience and evidence of effective independent research work within a research team, and contribution to the team effort. Evidence of ability to network and build collaborations.
• Demonstrated quality of research performance, as evidenced by high-quality publications in top-tier machine learning/computer vision/reinforcement learning and NLP 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).
• Ability to communicate complex information clearly, orally and in writing, in English.

The following desirable criteria will be evaluated by the level of proficiency. Recruitment will aim at selecting those candidates with the best possible performance in these criteria.

Desirable knowledge, skills, and experience are:

• Substantial previous research component in systems, on-device development, machine learning hardware or edge devices.
• Broad knowledge of machine learning methods beyond modern deep learning methods. Knowledge of classical machine learning approaches and their foundations.
• Understanding of multi-agent systems and game theory.

The salary for this post is £40,247 - £47,874 per annum.

Click to view a copy of the full job description (opens new browser tab)

Please ensure you include the following documents in your application:

- CV

- Cover letter

If you are interested in this post then it is recommended you make informal enquiries to Amos Storkey <amos+dAIEdge@inf.ed.ac.uk> for any further information, alongside making the application.

As a valued member of our team you can expect: 

• A competitive salary
• An exciting, positive, creative, challenging and rewarding place to work. 
• To be part of a diverse and vibrant international community
• Comprehensive Staff Benefits, such as a generous holiday entitlement, competitive pension schemes, staff discounts, and family-friendly initiatives. Check out the full list on our staff benefits page (opens in a new tab) and use our reward calculator to discover the total value of your pay and benefits 

Championing equality, diversity and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality. 

Prior to any employment commencing with the University you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages (opens new browser tab)

The University is able to sponsor the employment of international workers in this role.  If successful, an international applicant requiring sponsorship to work in the UK will need to satisfy the UK Home Office’s English Language requirements and apply for and secure a Skilled Worker Visa.  

Key dates to note

The closing date for applications is 29 November 2024.

Unless stated otherwise the closing time for applications is 11:59pm GMT. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone. 

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