Type of research degree: PhD
Application deadline: Ongoing
Country eligibility: UK/EU (full) or International (only covering tuition fees)
Funding: Funded (UK/EU) or partially funded (international)
Prof Ronghui Liu and Dr Zhiyuan Lin
Institute for Transport Studies, University of Leeds, UK (with up to 1 year exchange opportunity at TU Delft, the Netherlands)
There is an exciting opportunity for a motivated student with research interests in optimisation, AI and their applications in transport planning.
Applications are invited for a PhD studentship in innovative approaches in artificial intelligence for railway scheduling and operations, to be based in Institute for Transport Studies at University of Leeds. The position is an opportunity to combine cutting-edge research at the intersection of railway scheduling and artificial intelligence techniques such as machine learning, neural networks. The overall objective of the PhD research project is to investigate the potential of Artificial Intelligence (AI) in the rail sector and contribute to the definition of roadmaps for future research in operational intelligence and network management. In particular, the student will develop and compare different AI approaches, e.g. machine learning, deep and reinforcement learning, for railway traffic planning and management. He or she will have a chance to investigate using AI for solving combinatorial optimization problems, AI for supporting optimization models, with special focus on the optimization models for railway operations and management.
This PhD research forms part of the RAILS project, funded by EU Shift2Rail and in collaboration with other European universities from Italy, Sweden and the Netherlands. The student will have the opportunity to work with the academic leads from these institutions, including spending up to one year at Digital Rail Traffic Lab at TU Delft (www.tudelft.nl/drtlab). The project is suitable for a student with a top MSc (preferable) or first-class bachelor’s degree in artificial intelligence, transport, computer science, operational research (optimisation), mathematics, physics, or a subject of highly quantitative nature. Previous coursework or experiences in at least one of the above areas is necessary. A strong programming background will be essential for this project. Prior experiences in one or more of the following areas is desirable but not mandatory: supervised machine learning in transport prediction, transport timetabling/scheduling, and applying artificial intelligence in theoretical or real-world combinatorial optimisation problems.
To apply for the scholarship, please visit https://environment.leeds.ac.uk/transport-research-degrees/doc/apply-2. For informal enquiries about the position, please contact Professor Ronghui Liu (R.Liu@its.leeds.ac.uk) or Dr Zhiyuan Lin (Z.Liu@leeds.ac.uk) with a short summary of your background and research interests in the technical themes mentioned above.
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