Research Fellow in Machine Learning for Online Production Scheduling

The project is funded by the Engineering and Physical Sciences Research Council (EPSRC) and is in collaboration with investigators from University of Manchester (UK). The project aims at developing next-generation machine learning enabled techniques for online production scheduling within the process industries.

The main role of the postholder will be to carry out research in the areas of data-driven optimization, reinforcement learning and mixed integer programming for production scheduling problems.

Other duties are:

To develop and implement novel data-driven methods for production scheduling in process industries under uncertainty.

Develop and apply reinforcement learning methods among others for online rescheduling problems.

Plan and prepare papers from own and related research to appropriate peer reviewed journals, international conferences and research seminars.

Collaborate with other PDRAs and PhD student team members for exchange of knowledge, best practice, as well as to carry out coordinated pieces of research.

The duration of this post is 24 months in the first instance.

A PhD in Engineering discipline, Operations Research or Computer Science. The applicant must either have already completed a doctorate, or have submitted their thesis prior to taking up the post. Proven publications track record in developing novel techniques for process scheduling and/or data-driven optimization and/or mathematical programming. Experience in reinforcement learning or data-driven optimization methods. Experience of working in a multi-disciplinary research environment so as to conduct effectively collaborative research. Experience with optimization packages (e.g. GAMS, Pyomo). Experience in programming languages (e.g. Python, Julia), machine learning packages (e.g., Keras/PyTorch/OR-gym) and maintaining online repositories (e.g. GitLab/GitHub) Ability to present complex information effectively to a range of audiences.

Applicants should apply online. To access further details about the position and how to apply please click on the ‘Apply’ button above.

If you have any queries regarding the vacancy or the application process, please contact Dr. Vasileios Charitopoulos at v.charitopoulos@ucl.ac.uk.

Latest time for the submission of applications: 23:59.

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