Research Fellow in optimisation under uncertainty for hydrogen-led decarbonisation strategies

Chemical Engineering

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UCL, Gower Street, London, UK


  • Posted on: 08th Feb 2021
  • Salary: £36,028 - £38,015 per annum
  • REF: 1874590
  • Closes: 08th March 2021
  • Contract Type: Fixed-Term/Contract
  • Hours: Full Time

Duties and Responsibilities

This position is in the Product and Process Systems Engineering Group. The investigators are members of the Centre for Process Systems Engineering (CPSE), a joint centre between Imperial College London and University College London.

The remit of this role is within the 1.5M EPSRC project Hydrogen Infrastructure Uncertainty Management for Heat Decarbonisation (HUMAN). The project considers tactical and operational decisions related to the deployment of a hydrogen-led system and its interaction with the power grid across spatial and temporal scales. Employing the tools developed within the project the optimal mix of electrification and hydrogen-based decarbonisation of heat will be explored for different regions in the UK.

Key responsibilities include:

To develop and implement novel methods for multi-stage optimisation under uncertainty.

To develop and implement machine learning approaches for quantifying and incorporating uncertainty considerations in optimisation problems.

To develop and implement decomposition/iterative techniques for the solution of large-scale energy systems models.

To plan and execute real options analysis of hydrogen supply infrastructure to account for and manage risk under different scenarios (including use of hydrogen only for transport, or use if for both transport and heat).

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.

Further information can be found at the Job Description document.

Duration of the role is for 24 months in the first instance.

Key Requirements

The successful candidate must have a PhD in Engineering, Operations Research or Computer Science with advanced understanding of supply chain optimisation and optimisation under uncertainty is required. 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 optimisation under uncertainty and/or the efficient solution of stochastic/robust optimisation programs. Experience in machine learning for uncertainty quantification and modelling. Experience with energy systems models (e.g. UK TIMES) and complex energy datasets. Proficiency with optimisation packages (e.g. GAMS, Pyomo). Experience in programming languages (e.g. Python, Julia) and maintaining online repositories (e.g. GitLab/GitHub). Candidates must have excellent communication and team-working skills, the ability to write high-standard reports and work collaboratively with colleagues to generate high-quality research outputs.

Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment Please note: appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at research assistant Grade 6B (salary 31,542 – 33,257 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.

Further Details

A job description and person specification can be accessed at the bottom of this page.

To apply for the vacancy please click on the Apply Now button below.

If you have any queries regarding the vacancy or the application process, please contact Dr. Vasileios Charitopoulos at or Prof. Lazaros Papageorgiou at

UCL Taking Action for Equality

Closing Date

8 Mar 2021

Latest time for the submission of applications


Interview date


Our department holds an Athena SWAN Silver award, in recognition of our commitment and demonstrable impact in advancing gender equality.

This appointment is subject to UCL Terms and Conditions of Service for Research and Support Staff.

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