Work from Office
Full Time
Job Description Responsibilities: Build, train, and benchmark ML models (e.g. LSTM, XGBoost) for battery State-of-Health (SOH), Remaining Useful Life (RUL), and degradation prediction. Analyze large-scale battery performance data to extract actionable insights. Develop anomaly and event detection algorithms for safety-critical battery threats. Create predictive maintenance models to forecast battery failures and degradation. Validate and benchmark model performance using real-world and simulated datasets. Work with the product team to ensure integration of developed models. Stay updated with the latest research in battery modeling and machine learning techniques. Research and collaborate with the team to incorporate electrochemical knowledge into ML models. Qualifications Preferred Skills & Tools: Python (Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow) Strong grasp of time-series modeling, signal processing,
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