Research Fellow in Machine Learning-enabled Optimisation of Cryogenic Machining Processes

Do you want to join a multidisciplinary team of world-leading experts developing enhanced industrial machining processes of the future? Do you want to bring expertise in Machine Learning and Optimisation to assess sustainability goals of industrial processes?

Currently the dominant approach for cooling and lubricating machining processes, such as drilling, milling and turning, is to use emulsion-based coolants (otherwise known as metalworking fluids) at high flow rates. There are many serious environmental, financial and health and safety reasons for reducing industry's reliance on emulsion coolants. These issues have motivated extensive research efforts to identify more sustainable machining processes. There is growing and compelling evidence from preliminary studies that cryogenic machining with supercritical CO2 (scCO2) with small amounts of lubricant (Minimum Quantity Lubrication, MQL, referred to as scCO2+MQL machining) can provide a high-performing and more sustainable alternative. Current knowledge gaps in the relationships between key input and output variables, the reasons for variations in performance and concerns over the release of CO2, are preventing a major uptake of this technology by UK manufacturers.

You will take the lead in developing Machine Learning and Optimisation methods to maximise the engineering performance of this advanced machining process, accounting for environmental sustainability and economic factors and using unique data-sets generated from state-of-the-art experimental investigations at Leeds and machining trials at the AMRC. You will develop user-friendly software tools in Python, which will enable academic and industry stakeholders to use the outcomes of your research and present your research at key academic and industry meetings, in the UK and overseas.

You will work with a second postdoctoral researcher and with an integrated academic and industrial project team to develop new learning and to disseminate the project findings via publications and presentations.

 

What we offer in return 

25 days holiday plus 15.5 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s over 40 days a year! Pension schemes with a generous contribution from the University. Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls. Personal Development: Access to courses run by our Organisational Development & Professional Learning team, and self-development courses including languages, Creative Writing, Wellbeing Therapies and much more. Access to on-site childcare, shopping discounts and travel schemes are also available. 

And much more!   

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

Harvey Thompson, Professor of Computational Fluid Dynamics  

Tel: +44 (0)113 343 2136 or email: H.M.Thompson@leeds.ac.uk 

 

Closing Date: 17 Jul 2022

Section: Research

Salary: £34,304 to £40,927 p.a.