Fully Funded PhD: ML for Bio-Feedstock Corrosion

RFCSR; Leeds; On-site; GBP 19,000 - 25,000; Full time

Job summary

The University of Leeds – School of Mechanical Engineering in Leeds, UK, invites applications for a fully funded PhD studentship on Machine Learning Driven Corrosion Modelling in Bio‑Feedstock Refining. The project investigates how bio feedstocks affect refinery corrosion and develops data‑driven tools to predict and mitigate it, combining ML with physical chemistry.

You will collaborate with Imperial College London, UCL, UIUC, and BP, progress from statistics to hybrid and physics‑informed

Qualifications

• Strong analytical skills and data analysis experience.
• Interest in machine learning and data‑driven modelling.
• Ability to work collaboratively within international research teams and with industry partners.

Responsibilities

• Develop data‑driven models to predict corrosion in bio‑refinery environments.
• Progress from statistical methods to hybrid/physics‑informed models; engage in adaptive experimental strategies.

Skills

Data analysis

Machine learning

Python

MATLAB

Interdisciplinary collaboration

Education

Bachelor's degree or MSc

Tools

Python

MATLAB

Job description

The University of Leeds – School of Mechanical Engineering in Leeds, UK, invites applications for a fully funded PhD studentship on Machine Learning Driven Corrosion Modelling in Bio‑Feedstock Refining. The project investigates how bio feedstocks affect refinery corrosion and develops data‑driven tools to predict and mitigate it, combining ML with physical chemistry.

You will collaborate with Imperial College London, UCL, UIUC, and BP, progress from statistics to hybrid and physics‑informed