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ROLE: Senior Battery Algorithm Engineer (Senior to Principal Considered)
LOCATION: Oxfordshire
COMPENSATION: Market Leading
TECHNE is supporting an advanced Battery Intelligence technology company in the search for a Senior Battery Algorithm Engineer to join their growing engineering team in Oxfordshire.
This role sits at the forefront of next-generation Battery Management Systems, focused on developing advanced embedded algorithms that directly impact battery safety, fast charging performance, degradation detection, lifetime optimisation, and real-world reliability.
You will work on highly complex nonlinear systems across temperature, ageing, and operational variability, translating advanced theory into embedded, production-ready solutions.
Key Responsibilities:
- Develop advanced battery state estimation and control algorithms across multiple chemistries and operating conditions
- Design diagnostics and prognostics algorithms for next-generation BMS platforms
- Build robust observer architectures using EKF, UKF, Kalman Filters, MPC, and probabilistic estimation techniques
- Deploy real-time embedded algorithm solutions
- Lead simulation-based validation activities using representative drive cycles and ageing scenarios
- Analyse cell, module, and pack-level datasets to identify performance limitations and edge cases
- Support algorithm validation through cell testing and data interpretation
- Collaborate with modelling, validation, embedded software, and systems engineering teams
- Produce technical documentation covering validation, assumptions, and performance metrics
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Required Experience:
- Degree in Mathematics, Physics, Electrical Engineering, Mechanical Engineering, Statistics, Computer Science, or related STEM discipline
- Experience developing estimation or control algorithms using EKF/UKF, Kalman Filters, MPC, or similar approaches
- Strong understanding of nonlinear systems and estimation theory
- Strong analytical and problem-solving capabilities
- Experience working within multidisciplinary engineering environments
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Desirable Experience:
- Battery algorithms including SOC, SOH, and SOP estimation
- Physics-based battery modelling including DFN or SPM
- MATLAB/Simulink and model-based development
- PyBaMM, COMSOL, or similar battery modelling platforms
- Embedded systems and embedded software deployment
- Data-driven modelling, Gaussian Processes, or embedded ML
- Battery testing and validation
- ASPICE and CI/CD environments such as GitHub, GitLab, or Azure DevOps
This is an opportunity to join a high-performing engineering environment developing advanced battery intelligence technology with direct impact on future mobility and energy systems.
If you would like to discuss the position confidentially, please get in touch directly
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