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PhD in Deep Reinforcement Learning for Smart Steel Processing

PhD in Deep Reinforcement Learning for Smart Steel Processing


Project Overview

Sequential decision making describes a situation where the decision maker makes successive observations of a process before a final decision is made. With recent advanced in artificial intelligence, and especially “deep learning” (artificial neural networks), much progress has been made in developing computer agents that are able to make sequential decision on their own. In this PhD project we will develop advanced artificial intelligence algorithms for sequential decision making for applications in smart steel processing.

The PhD project is part of SUSTAIN, an EPSRC-funded programme co-created by the 5 major UK steel producers (Tata, Liberty, British Steel, Celsa, Sheffield Forgemasters) and the three principal Universities that have expertise in this area (Swansea, Warwick and Sheffield) to provide academic leadership in the field of steel innovation. Although the steel industry has generated “big data” for over 30 years, the production benefits have been limited so far. In this PhD project, we will develop novel data-driven techniques that leverage the latest advances in data science and machine learning. Ultimately, we will deliver an AI system for Smart Steel Processing able to automate and optimise certain processes that still rely heavily on manual intervention. We will exploit existing historical data repositories made available by our industrial collaboration and the availability of next-generation sensors that are now replacing traditional sampling methods in extreme environments. We will also develop a “digital twin”, a simulation-based environment to help us test and develop novel reinforcement learning algorithms.

Essential and desirable criteria

A minimum 2.1 undergraduate (BEng, MEng, BSc) and/or postgraduate masters’ qualification (MSc) in science and technology field: Computer Science, Engineering, Mathematics, ideally with specialisation in Machine Learning and AI. Familiarity with machine learning and probabilistic models is ideal.


Funding and Eligibility

Funding of £18,009 per annum is available for UK/EU applicants for 3.5 years.

To be eligible for this project the successful applicant should have indefinite leave to remain in the UK and have been ordinarily resident here for 3 years prior to the project start-date, apart from occasional or temporary absences. Additional details of these criteria are available on the EPSRC website.


To apply

To apply please complete our online enquiry form and upload your CV.

Please ensure you meet the minimum requirements before filling in the online form.

Key Information:

Funding source: EPSRC-funded programme

Stipend: £18,009 for 3.5 years

Supervisors: Professor Giovanni Montana and Professor Claire Davis

Available to UK/EU nationals

Start date: We would ideally like someone to start as soon as possible, but we can offer some flexibility on this - please contact us to discuss.