Research Fellow

Salary: 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.

Contract Type:  Fixed Term contract up to September 2028

Background

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.

Role Summary

• 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.

Main Duties

Data Engineering & Preparation

• Develop automated pipelines for ingesting, cleaning, and structuring data from sensors, electrochemical testers, imaging systems, and environmental logs.
• Establish metadata standards and ensure datasets meet FAIR principles (findable, accessible, interoperable, reusable).
• Create high-quality, ML-ready datasets through feature extraction, multivariate analysis, and robust quality control workflows.

Modelling & Machine Learning

• Develop hybrid models that combine domain physics with data-driven techniques (e.g., PINNs, surrogate models, Bayesian optimisation).
• Work with experimental partners to interpret formation signatures and validate model outputs against real-world measurements.
• Build interpretable, mechanistically grounded models to identify early predictors of formation success and inform protocol optimisation across the consortium.

Person Specification

Essential Qualifications

• PhD (or near completion) in engineering, computer science, physics, applied mathematics, or a related discipline with a strong data-driven or machine learning focus.

Essential Skills & Experience

• Strong programming skills (Python essential) and experience with scientific computing libraries.
• Experience building or working with complex datasets, ideally from experimental or sensor-based systems.
• Hands-on experience with machine learning, including deep learning, probabilistic models, or physics-informed approaches.
• Ability to analyse, visualise, and interpret complex time-series or multi-modal data.
• Strong communication skills, including the ability to explain technical concepts to non-experts.

Informal enquiries to Niels Lohse, email: n.lohse@bham.ac.uk 

To download the full job description and details of this position and submit an electronic application online please click on the 'Apply' button above.

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