This post is funded through an EPSRC- Imperial UKRI Impact Acceleration Account (IAA) award and focuses on the development of predictive, industry-facing computational models for spray and mixing systems. The role sits at the interface of Computational Fluid Dynamics (CFD), machine learning, and optimisation, with applications spanning biopharmaceutical manufacturing and agrochemical delivery.
The role offers a unique opportunity to contribute to a translational modelling and optimisation platform with clear industrial impact.
You will develop and apply machine learning–based optimisation frameworks integrated with Computational Fluid Dynamics (CFD) models for spray and mixing systems. You will implement and maintain coupled CFD–PBM–ML workflows to predict and control droplet and particle size distributions, adopting a code-fast, test-fast, learn-fast approach to rapid prototyping and model refinement. The role involves applying these methods to industry-facing case studies.
The successful candidate will have strong scientific programming skills in C/C++ and Python, thrive in a rapid, iterative research environment, and adopt a code-fast, test-fast, learn-fast approach to development. Importantly, they will be curious about how research becomes technology, showing initiative, ownership, and an entrepreneurial mindset, and will enjoy working closely with industrial partners on problems with clear routes to deployment and future commercialisation.
This is a full-time post (35 hours per week) offered as a fixed-term contract for 12 months. The role is hybrid-working at our South Kensington Campus.
If you require any further details about the role, please contact: Prof. Omar Matar – o.matar@imperial.ac.uk or Dr Nausheen Basha Nausheen.basha@imperial.ac.uk.
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