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About Fawkes Energy
Batteries are the most valuable and least understood asset in the electric mobility ecosystem.
At Fawkes Energy, we're building the intelligence layer for batteries, combining electrochemistry, physics-based models, operational data, and machine learning to unlock battery health, degradation, safety, reliability, residual value, and lifecycle insights across EVs and energy storage systems.
We're looking for someone who can connect what happens inside a battery at the electrochemical level with what we observe in real-world operational data. If you enjoy understanding why batteries behave the way they do and not just building models, but uncovering the underlying physics then this role is for you.
What you'll do
Decode
- Analyze large-scale battery telemetry datasets from EVs and energy storage systems.
- Interpret voltage, current, temperature and environmental conditions to understand battery behaviour.
- Connect operational signals to underlying electrochemical mechanisms and degradation pathways.
Algorithms
Develop algorithms for:
- State of Health (SOH)
- Remaining Useful Life (RUL)
- Capacity Fade
- Resistance Growth
- Cell Imbalance Detection
- Safety & Thermal Risk Detection
- Fault Diagnostics & Prognostics
- Create context-aware models that understand how operating conditions influence battery ageing.
Physics Informed Models
- Combine first-principles battery physics with data-driven methods.
- Work with equivalent circuit models, reduced-order electrochemical models, and battery digital twins.
- Leverage tools such as PyBaMM and custom modeling frameworks.
- Build hybrid Physics + AI approaches that outperform purely statistical models.
Feature Extraction
Apply battery diagnostics techniques:
- Incremental Capacity Analysis (ICA)
- Differential Voltage Analysis (DVA)
- OCV-based methods
- Resistance estimation
- Relaxation analysis
- Coulombic efficiency analysis
- Feature engineering from charge/discharge curves
- Design novel health indicators and degradation signatures
Research
- Read and evaluate academic literature critically.
- Develop novel methods to solve real-world battery problems.
- Translate research into deployable algorithms.
- Publish technical articles, patents, white papers, and technical documentation where applicable.
Qualifications
Required
- PhD in Electrochemistry, Battery Science, Chemical Engineering, Electrical Engineering, Applied Physics, Energy Storage, or a closely related field.
OR
- Master's degree with 3-6 years of demonstrated experience in battery modeling, battery analytics, degradation analysis or battery diagnostics
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