AI/ML Research Intern

Position: AI/ML Research Intern (Battery Analytics)

Duration: 3–6 Months (Full-time)

Location: Barrackpore, Kolkata

Target Audience: 3rd or 4th Year BTech Student (CSE/IT/ECE/EE/ME/Materials Science)

Responsibilities

Data Preprocessing: Clean, label, and restructure raw battery cycling data (from public datasets like NASA, Oxford, or custom lab data).

Feature Engineering: Extract key health features (e.g., incremental capacity, charge/discharge time, temperature spikes) that correlate with degradation.

Model Development: Implement machine learning algorithms (e.g., LSTM, RNN, Gaussian Process Regression (GPR), or Random Forests) to forecast battery SOH/RUL.

Validation: Evaluate model performance using metrics such as RMSE, MAE, and MAPE.
Documentation: Document the preprocessing pipeline, model architecture, and results.

Requirements

Education: Currently pursuing 3rd or 4th year BTech in Computer Science, IT Engineering, ECE, Electrical Engineering, Mechanical Engineering, or related fields.

Programming: Proficiency in Python and libraries like Pandas, NumPy, Scikit-learn, and TensorFlow or PyTorch.

ML Knowledge: Solid understanding of supervised learning, regression, and time-series analysis.

Domain Interest: Basic familiarity with Lithium-ion batteries or a keen interest in electrochemistry and battery management systems.

Preferred Skills (Nice to have)
Experience with LSTM (Long Short-Term Memory) networks.
Knowledge of MATLAB/Simulink for data processing.
Understanding of physical degradation mechanisms (SEI layer growth, lithium plating).

Perks

Mentorship from industry experts in AI and Energy Storage.
Opportunity to publish findings or include in final year project.
Pre-placement Offer (PPO) based on performance.
Stipend
Certificate after completion