Research Fellow in Materials Modelling

About us

The UCL Faculty of Engineering Sciences brings together ten academic departments undertaking world-leading research and teaching across a broad range of engineering disciplines. At UCL Mechanical Engineering, we develop innovative solutions to some of society’s most pressing challenges, from low-carbon shipping to sustainable propulsion systems and fuels.

This post forms part of a major industry-sponsored collaboration between UCL, bp, the University of Leeds, Imperial College London and the University of Illinois Urbana-Champaign focused on supporting the transition to sustainable aviation fuels (SAF). As SAF adoption increases, understanding how bio-feedstocks affect material corrosion is critical to ensuring the long-term integrity of refinery infrastructure and components. The project will develop advanced modelling tools to predict corrosion and material degradation, supporting the deployment of digital corrosion-management solutions.

About the role

We are seeking a Postdoctoral Research Fellow to join the Applied Metallics Group and contribute to a large-scale collaborative programme investigating bio-feedstock-induced corrosion. Working closely with researchers across academia and industry, you will develop physics-based and physics-informed machine learning models to predict corrosion across a range of materials and processing environments.

Key responsibilities include assessing and advancing thermodynamic, continuum and atomistic modelling approaches; developing predictive models for corrosion behaviour and material degradation; creating physics-informed machine learning frameworks trained on experimental data; and using these tools to identify material descriptors relevant to corrosion performance. The postholder will work closely with industrial and academic partners, disseminate findings, and contribute to high-quality scientific publications.

About you

You will hold a PhD, or possess equivalent research experience, in a relevant discipline such as materials science, mechanical engineering, computational engineering or a related field. You will have strong expertise in machine learning applied to materials design, optimisation or degradation studies, alongside demonstrable experience of multiscale modelling approaches, including atomistic modelling or related computational techniques.

The successful candidate will have a proven ability to develop and deliver independent research programmes addressing complex materials challenges, supported by a strong publication record in peer-reviewed journals. Experience applying multiple computational approaches to materials research is essential, as is the ability to work effectively within interdisciplinary teams. You will also have experience collaborating with industry and experimental researchers, reflecting the highly collaborative nature of this project.

Further information

This Grade 7 position is available for 22 months and is primarily based onsite at UCL’s Bloomsbury campus. The post is funded at Spine Point 32 (£47,186 per annum, inclusive of London Weighting).

Applicants should submit a CV and supporting statement addressing each criterion in the Job Description and Person Specification, as applications will be assessed against these requirements.

Equality, Diversity and Inclusion

As London’s Global University, UCL recognises that diversity of thought, background and experience strengthens research, teaching and innovation. We are committed to creating an inclusive environment where everyone can thrive and particularly welcome applications from groups currently underrepresented within our workforce.

UCL Mechanical Engineering holds an Athena Swan Bronze Award in recognition of its commitment to advancing gender equality and fostering an inclusive culture for staff and students.

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