PhD Studentship – Machine Learning Driven Corrosion Modelling in Bio‐Feedstock Refining

Overview

University of Leeds – School of Mechanical Engineering, Leeds, United Kingdom

General Description – This fully funded PhD studentship at the University of Leeds offers a research opportunity focused on Machine Learning Driven Corrosion Modelling in Bio‑Feedstock Refining. The project addresses the engineering challenge posed by corrosion in next‑generation bio‑based fuel processing. Bio feed stocks often exhibit behaviours distinct from traditional crude oil, which can accelerate corrosion in refinery environments. The successful candidate will develop advanced data‑driven tools that predict and manage corrosion in these complex systems, integrating modern machine learning techniques with physical and chemical understanding.

Regular collaboration with researchers from Imperial College London, University College London, and the University of Illinois at Urbana‑Champaign, alongside industrial scientists at BP, will support a multidisciplinary research context. The work will progress from initial statistical and machine learning methods to hybrid and physics‑informed models, contributing to adaptive experimental strategies and high‑throughput assessment approaches relevant to sustainable bio‑refineries. The role includes opportunities to present findings, publish research, and engage with industrial challenges in sustainable technology and corrosion management.

Applicants should hold a first‑class or upper second‑class honours bachelor’s degree (or equivalent), or a relevant master’s degree, in disciplines such as engineering, materials science, chemistry, physics, computer science, applied mathematics, or related fields. Candidates must be eligible to pay tuition fees at the UK fee rate; the studentship is open only to UK applicants at the UK fee status. Applicants who have previously been awarded a PhD or are currently registered for a PhD are not eligible.

• Strong analytical skills and a foundational understanding of data analysis and modelling.
• Experience or familiarity with programming languages such as Python or MATLAB is advantageous.
• Interest in machine learning, data‑driven modelling, corrosion science, and interdisciplinary research.
• Ability to work collaboratively within international research teams and engage with industrial partners.

The studentship includes full academic fees coverage and a tax‑free maintenance grant at the standard UKRI rate of £21,805 per year for 3.5 years.

Applications must be submitted by Friday 26 June 2026.