PhD studentship in AI-based smart energy

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The machine learning (Mlearn) group at the University of Lincoln,UK, is looking for PhD students in Smart Energy, Artificial Intelligence and Optimization. The positions are directed to promising young students.
Studentship covers 3 and half years of tuition fees at UK/EU student rates, a tax-free stipend at EPSRC rates, and a generous research training support grant enabling international travel and participation in the leading conferences and symposia.Studentship applications are now open for entry into the DTP programme, idealy starting in September 2020.Please note: Due to funder restrictions, we are unable to accept applications from non-UK/EU applicants. Additionally due to current circumstances interviews may take place online.

Closing date: Midnight, June 14th 2020.
Project title: AI-based Multi-objective Decision Making for Efficient Energy Management of Smart GridsAcademic Contact: Dr Shouyong Jiang ( project will focus on efficient AI-based energy management of smart grids to reduce energy costs and lower carbon footprint. In particular, it will investigate novel AI-based decision-making strategies trade-offing the profit of energy suppliers and the cost of energy users. This work involves computational modelling of smart grids in different scenarios and optimisation of energy management in each scenario. The project will develop an AI-based decision-making tool for smart grids that will be experimentally tested and analysed in order for academic and commercial use.The successful candidate will work with the Machine Learning group at the School of Computer Science and School of Engineering at the University of Lincoln. This is an exciting opportunity for developing a career in AI for smart energy.Specific requirements for candidates:Interested applicants should hold, at a minimum, a 2.1 degree in AI, computer science, mathematics, engineering, or any other relevant discipline and are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team, must be able to carry out mathematic modelling for practical problems, and have a good knowledge of operational research and optimisation approaches, such as evolutionary computation, and multi-criteria decision making. They are expected to have good communication skills in written and spoken English in order to work with both computer scientists and engineers, to present research findings in workshops/conferences, and to publish papers in high-quality journals.
General information about the project, candidates will find on

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