Machine Learning Engineer (Battery Diagnostics) Fresher Job

Job Description

About the job: Key responsibilities: 1. Analyze large datasets from Li-Ion battery testing, including data from Incremental Capacity Analysis (ICA), Differential Voltage Analysis (DVA), Electrochemical Impedance Spectroscopy (EIS), and long-term cycling studies 2. Develop and implement machine learning models (supervised and unsupervised) to predict battery State-of-Health (SoH), State-of-Charge (SoC), and Remaining Useful Life (RUL) 3. Integrate physics-based models (e.g., equivalent circuit models, electrochemical models) with machine learning approaches for hybrid diagnostics and prognostics 4. Extract and engineer diagnostic features from raw battery data to identify degradation patterns and failure modes 5. Collaborate with hardware and embedded teams to integrate diagnostic algorithms into embedded platforms and cloud-based analytics tools 6. Visualize and communicate complex data insights effectively through dashboards, reports, and presentations 7. Stay updated with the latest research in battery diagnostics, degradation modeling, and advanced analytics Requirements: 1. Degree in electrical engineering, mechanical engineering, chemical engineering, computer science, data science, or a related field 2. Strong foundation in Machine Learning, Deep learning (RNN, CNN, GNN, Transfer Learning, etc.) 3. Strong knowledge of Li-Ion battery degradation mechanisms, including calendar aging, cycle aging, and thermal effects 4. Proficiency in Python (NumPy, pandas, scikit-learn, TensorFlow/PyTorch, matplotlib) and MATLAB/Simulink for modeling and data analysis 5. Hands-on experience with battery characterization techniques: ICA, DVA, EIS, OCV-SoC mapping, DCIR measurements 6. Familiarity with Kalman Filters, Particle Filters, and advanced machine learning models for time-series forecasting and anomaly detection Who can apply: Only those candidates can apply who: Salary: ₹ 5,00,000 - 8,00,000 /year Experience: 0 year(s) Deadline: 2025-06-18 23:59:59 Skills required: MATLAB, Python, Machine Learning, UI & UX Design, Simulink, Data Visualization, Amazon Web Services (AWS), Deep Learning, Battery Management Systems, RNN (Recurrent Neural Network), CNN (Convolutional Neural Network) and Plotly About Company: ThinkClock Battery Labs is an R&D-driven company focused on battery health analytics using spectroscopy techniques, namely EIS (electrochemical impedance spectroscopy), acoustic, and RF spectroscopy. We reveal the diagnostic insight about the battery health while relating the microscopic data of the battery to the observable system-level behavior with the help of the models based on physics and machine learning. ThinkClock offers lab-based battery characterization services (for performance and uniformity assessment) to cell OEMs and battery pack manufacturers. We are creating a cell benchmarking database for the commercially available cells using the lab-based characterization data to provide an independent source of truth for the system integrators. While leveraging our cell benchmarking database, we have developed a portable tool, CellScope, which can quickly assess the health of a cell using spectroscopic techniques.