Research Fellow - School of Mechanical Engineering Grade 7

Job Description Position Details

School of Mechanical Engineering

Location:

University of Birmingham, Edgbaston, Birmingham UK

Full time starting salary is normally in the range £36,636 to £46,049 with potential progression once in post to £48,822. As this vacancy has limited funding the maximum salary that can be offered is Grade 7, salary £42,254.

Full Time, Fixed Term contract up to September 2028

Closing date: 22nd March 2026

The FAST (Formation and Ageing for Sustainable Battery Technologies) project is a major Faraday Institution consortium led by the University of Birmingham with partners across Oxford, Cambridge, Warwick, Nottingham, Imperial and UKBIC. Its mission is to transform the battery formation and ageing stages-currently the most time-, energy- and cost-intensive steps in lithium-ion cell manufacturing-by building a scientifically informed and scalable framework for next-generation production.

A key workstream of the fast FAST project provides the digital and analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during formation and to optimise process protocols. The Research Fellow will play a central role in this work.

The post holder will design and implement data extraction and preparation pipelines for heterogeneous datasets spanning electrochemical testing, embedded sensors, environmental logging, spectroscopy and advanced imaging. They will create and curate structured, FAIR‑compliant datasets suitable for multivariate analysis and machine learning, ensuring high‑quality metadata, traceability and reproducibility.

Building on this data foundation, the Fellow will develop hybrid modelling tools that integrate physics‑based insights with data‑driven methods—such as physics‑informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell performance, and reduce reliance on empirical testing. Working closely with engineers, modellers and experimentalists, they will generate interpretable, scientifically grounded models that directly inform the design of improved formation and ageing protocols.

• Work within the FAST (Formation and Ageing for Sustainable Battery Technologies) research programme, delivering the data engineering and modelling tasks that underpin Workstream 1b, and contribute to preparing project reports, presentations, and future funding proposals.
• Operate within the specialist area of data engineering, machine learning (ML), and physics‑informed modelling, applying advanced computational methods to heterogeneous battery formation datasets generated across the consortium.
• Analyse, interpret, and integrate multi‑modal research findings— including electrochemical time‑series data, imaging outputs, embedded sensor measurements, and environmental logs—to create structured, interpretable, and reusable datasets that support hybrid modelling.
• Contribute to generating funding by co‑authoring sections of new research proposals, demonstrating how data workflows, digital infrastructure, and ML approaches can support emerging research directions in battery manufacturing, diagnostics, and sustainable engineering.
• Contribute to pathways for commercial translation, including opportunities for software tools, modelling frameworks, or data pipelines to feed into licensing, future spin‑out activities, or industrial adoption by partners such as Agratas, Volklec, the UK Battery Industrialisation Centre (UKBIC), Gaussion, Illumion, and Oxford Battery Developments.
• Support public understanding and dissemination of the discipline by contributing to open‑source data standards, transparent modelling documentation, FAIR (Findable, Accessible, Interoperable, Reusable) datasets, and accessible explanations of physics‑informed ML models for academic and industrial audiences.

• Develop automated pipelines for ingesting, cleaning, and structuring data from sensors, electrochemical testers, imaging systems, and environmental logs.
• E…