Research Fellow in Machine Learning-enabled Optimisation of Engineering Flow Systems

Research Fellow in Machine Learning-enabled Optimisation of Engineering Flow Systems

Are you an enthusiastic and ambitious researcher looking for your next challenge? Do you have a background in Machine Learning and Flow Simulation? Do you want to further your career in one of the UK’s leading research intensive Universities?

You will support the research of Professor Harvey Thompson, Head of School of Mechanical Engineering. You will have a strong focus on Machine Learning applied to the design and optimisation, ideally within the context of Computational Fluid Dynamic analyses of flow systems. This complements a range of other activities of CFD-enabled optimisation within the School in areas such as microfluidic heat transfer systems in e.g. electronics cooling, chemical processing or rapid diagnostics; in mitigating the effects of corrosion in engineering flow systems; and flow optimisation in filtration or pharmaceutical applications.

Holding a PhD (or an expectation that a PhD will be awarded soon) in Computer Science, Mathematics, Mechanical Engineering or a related discipline, you will have research expertise in the general area of Machine Learning and its application to data-driven surrogate modelling and/or design optimisation.

To explore the post further or for any queries you may have, please contact:

Professor Harvey Thompson, Head of the School of Mechanical Engineering

Tel: +44 (0) 113 343 2136

Email:

Further information

The Schools in the Faculty of Engineering & Physical Sciences are proud to have been awarded the Athena SWAN or Silver Award from the Equality Challenge Unit, the national body that promotes equality in the higher education sector. Our provides more information.

Location: Leeds - Main Campus
Faculty/Service: Faculty of Engineering & Physical Sciences
School/Institute: School of Mechanical Engineering
Section: Institute of Thermofluids
Category: Research
Grade: Grade 7
Salary: £33,797 to £40,322 p.a.
Post Type: Full Time
Contract Type: Fixed Term (Available from 1 February 2020, until 31 July 2021 (specialist skills))
Release Date: Friday 08 November 2019
Closing Date: Sunday 08 December 2019
Reference: EPSME1013
Downloads:

In your application, please refer to Polytechnicpositions.com