As a Research Associate in Data-Driven Optimisation, you will work at the interface of chemical engineering, machine learning and automation, to develop next-generation workflows for the design and scale-up of chemical processes. The role will focus on using high-density experimental data from transient flow systems together with advanced machine learning techniques, including multi-fidelity optimisation and large language model (LLM)-in-the-loop frameworks, to accelerate decision-making from laboratory screening through to industrial scale.
You will contribute to the development of ML-enabled models that combine automated building of reaction kinetics with scale-dependent phenomena, and explore how LLMs can support model selection, experiment planning and workflow coordination, across complex datasets and tools. While the role is computational, you will work closely with experimentalists and engage with real industrial case studies, ensuring that developed methods are robust, interpretable and relevant to manufacturing practice.
You will be part of a highly collaborative academic, industrial team, working closely with partners including Solve Chemistry, Almac and Mettler Toledo Autochem. This role offers a unique opportunity to apply cutting-edge AI methods to real chemical manufacturing challenges and to see research translated into industrial impact.
You will design and implement machine learning-driven workflows to support the optimisation and scale-up of chemical processes. This will include developing automated kinetic model generation algorithms, multi-fidelity optimisation strategies and data-driven methods that link laboratory-scale screening to performance at larger scales.
A key part of the role will be the exploration and implementation of large language model (LLM)-in-the-loop frameworks to support experiment planning, model selection, interpretation of results and coordination of complex experimental and modelling workflows. You will work hands-on with data, code and modelling tools, contributing to the integration of experimental platforms with in-silico reactor and process simulations.
You will collaborate closely with academic colleagues and industrial partners to co-design and validate methodologies using real industrial case studies, and you will communicate your findings through presentations, reports and peer-reviewed publications. The role will involve regular interaction with industry scientists and engineers, and you should feel confident explaining technical work to both specialist and non-specialist audiences.
1. Strong technical and scientific background in machine learning, with experience in data-driven modelling and a strong interest in large language models (LLMs) and their application to scientific and engineering workflows.
2. Solid foundations in mathematics, engineering, or a related discipline, with experience in developing, implementing and validating algorithms.
3. Confident coding skills (. Python) and familiarity with modern software development practices, including version control and reproducible workflows.
4. Ability to work effectively in a multidisciplinary environment, with excellent verbal and written communication skills and confidence engaging with academic and industrial stakeholders.
5. Comfort with, or a strong willingness to rapidly learn, core concepts in chemical engineering, reaction engineering and process scale-up.
6. Previous experience with, or enthusiasm to learn, chemical engineering modelling and simulation tools (. kinetic, reactor or process modelling software).
7. The opportunity to lead and take ownership of a cutting-edge project applying state-of-the-art machine learning to engineering challenges.
8. Direct collaboration with industry leaders, including Solve Chemistry, Almac and Mettler Toledo Autochem.
9. Opportunity to publish in leading AI/ML conferences and journals.
10. A supportive environment to grow and develop as a scientist, with access to mentoring and training.
11. Membership in the Optimisation and Machine Learning for Process Systems Engineering group and the Sargent Centre for Process Systems Engineering—the largest centre of its kind globally.
12. The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
13. Grow your career: gain access to Imperial’s sector-leading as well as opportunities for promotion and progression.
14. Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).
15. Be part of a diverse, inclusive and collaborative work culture with various and resources to support your personal and professional .
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £43,863 – £47,223 per annum.