Position Overview:

We are seeking a talented and motivated Postdoctoral Researcher to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries.

Key Responsibilities:

• Develop and implement machine learning algorithms for SOC and SOH estimation.
• Analyze large datasets from battery systems to improve model accuracy and performance.
• Conduct research on predictive analytics for real-time battery health monitoring and fault detection.
• Collaborate with embedded systems and hardware engineering teams to integrate AI models into the BMS.
• Optimize AI/ML pipelines for resource-constrained environments, including edge AI applications.
• Guide PhD students and collaborate with engineers to implement advanced ideas.
• Author scientific publications, present findings at conferences, and contribute to patents or technical innovations.

Qualifications:

• PhD in Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field.
• Strong experience in developing and applying AI/ML models to energy systems or similar applications.
• Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Hands-on expertise in programming languages such as Python, R, or MATLAB.
• Solid understanding of battery systems, electrochemical processes, or energy management.

Preferred Skills:

• Experience with time-series analysis, predictive modeling, and anomaly detection.
• Familiarity with real-time applications of AI/ML in embedded or IoT devices.
• Knowledge of cloud-based computing platforms for data processing (e.g., AWS, Google Cloud)
• Understanding of BMS architecture and electric mobility systems.