Research Fellow Computational Fluid Dynamics/Machine Learning

About us

UCL Mechanical Engineering has been pioneering the development of engineering education, having taught the core discipline for over 165 years.

Amongst many other avenues we are:

• Leading research into low carbon shipping
• Designing the greenest engines and novel sustainable fuels
• Biomechanical Research
• Developing and testing new and unique materials

This post is part of Air Safety project supported by NIHR, benefiting from a direct collaborations with a clinical team led by Professor Laurence Lovat (UCL Hospital). Our emphasis is on rapid clinical translation, with the research fellow concentrating on improving current computational modelling framework to rapidly predict aerosol dispersion in a variety of clinical scenarios.

In particular we are seeking an expert in computational fluid dynamics with experience of data-driven approaches, and/or an expert of physics-informed ML. The research fellow will work in close synergy with experimental fluid mechanics, materials, microbiology and nanotechnology experts, as well as clinicians to facilitate rapid translation of technology to clinic. The post is initially funded for two years and will be subject to standard probation and appraisal procedure

About the role

The post will underpin our activities in computational flow analysis focusing on aerosol droplet dispersion. We will be undertaking computational modelling of aerosol dispersion due to human breathing, towards understanding transmission risk of airborne diseases in hospital spaces. The modelling will be conducted for various clinical settings, first using computational fluid dynamics (CFD) which will then be accelerated using machine learning (ML), including physics-informed ML algorithms. The model-based predictions will be complemented by experimental measurements of aerosol dispersion, conducted by other project members. Our ultimate goal is to provide a tool for healthcare professionals to predict aerosol dispersion and impact of mitigations such as portable air purifiers.

About you

We would be delighted to receive applications from candidates who meet the following criteria:

• Holder of a PhD in fluid mechanics and/or related areas coupled with an excellent track record of research in computational fluid dynamics and/or physics-informed machine learning
• Track record of excellent publications.
• Interest in engaging in some department teaching activities.

Please follow the Apply button and refer to the attached Job Description & Person Specification for a comprehensive list of the skills and attributes required.

What we offer

As well as the exciting opportunities this role presents, we also offer some great benefits which are detailed on our Rewards and Benefits page.

Our commitment to Equality, Diversity and Inclusion

As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.

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