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Optimisation Under Uncertainty Using Machine Learning & Data-Driven Methods

  • Full or part time
  • Application Deadline
    Tuesday, March 31, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description


The UCL department of Chemical Engineering is one of the top research and teaching departments in the UK and has world-class standing. Within the Dept. of Chemical Engineering 90% of staff rated as world leading or of internationally excellent quality. The department has an extensive research portfolio across a wealth of areas, from molecular scale to complex systems.
Within the Product & Process Systems Engineering group, the Chemical Engineering Department is seeking an enthusiastic and dedicated post-graduate student to research how to incorporate uncertainty consideration related to the optimisation of process & energy systems engineering via novel data-driven and machine learning methods. Expertise in mathematical modelling & optimisation is essential, as well as a good understanding of machine learning techniques is desirable.
The post is fully funded (stipend and fees) for 4 years.

Please contact Dr Vasileios Charitopoulos () for further details or to express an interest.


We are seeking a motivated PhD candidate to work on the project “Optimisation under uncertainty using machine learning and data-driven methods” which is concerned with developing state-of-the-art computational methods for incorporating uncertainty considerations in industrially-relevant complex optimisation problems and tools for their efficient solution.

Candidates with experience/expertise in the following areas are encouraged to apply for the studentship:
• Mathematical modelling & optimisation
• Optimisation under uncertainty
• Machine learning & data-driven modelling

The candidate will explore:
• Novel methods for modelling uncertainty considerations & propagation in multi-period optimisation problems;
• Machine learning oriented dimensionality reduction techniques;
• Mathematical programming solution methods for stochastic programming problems;
• Implementation of the aforementioned into state-of-the-art process & energy systems engineering problems.


The successful applicant will have a 1st class MSc/MEng (or equivalent) in an area pertinent to the subject area, e.g. Chemical Engineering, Operations Research, Computer Science, Mathematics.
All applicants should be able to demonstrate the following:
• A strong computing background with solid mathematical skills,
• Optimisation and/or machine learning experience,
• Advanced understanding of process and/or energy systems simulation/optimisation problems
• The ability to work independently and to drive both the research and software development agenda.

Funding Notes


First-class degree at the MEng/MSc or EU equivalent level is required.
Funds are only available to cover EU/UK fees.

Stipend: £17,264 per annum + UK/EU fees

Start date: the successful candidate is expected to start on 01/10/2020

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