Research associate machine learning

Location: South Kensington Campus About the role: 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. What we can offer you: The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity. Work closely with one of the world-leading pharma companies. Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression. Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes). Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing. Further Information 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 – [email protected] or Dr Nausheen Basha [email protected] If you experience any technical issues while applying online, please don't hesitate to email us at [email protected]. We're here to help.