[Jobs] - Imperial College London - Research Associate in Machine Learning
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Nausheen Basha
Feb 24, 2026, 8:04:44 PM (2 days ago) Feb 24
to Women in Machine Learning
About the role
Based at Imperial College London, 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.
What you would be doing
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.What we are looking for
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.
Application link : https://www.imperial.ac.uk/jobs/search-jobs/description/index.php?jobId=27001
Based at Imperial College London, 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.
What you would be doing
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.What we are looking for
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.
Application link : https://www.imperial.ac.uk/jobs/search-jobs/description/index.php?jobId=27001
Deadline: 15 March 2026
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