Postdoctoral Researcher, AI/ML for Battery Management Systems

About the role

The Green Tech Institute at Mohammed VI Polytechnic University (UM6P), located in Benguerir, Morocco, is hiring a postdoctoral researcher to develop AI and machine learning models for battery management systems (BMS) in electric and micro-mobility applications. The focus is on state-of-charge (SOC) and state-of-health (SOH) estimation for lithium iron phosphate (LFP) batteries. UM6P is a research-focused university dedicated to Africa’s energy transition, situated near major solar energy infrastructure in southern Morocco. The position is open until filled; apply as early as possible.

Responsibilities

• Develop and implement machine learning algorithms for real-time SOC and SOH estimation using data from LFP battery cycling experiments.
• Analyse large-scale datasets from EV and micro-mobility test benches to improve model accuracy across a range of temperatures and usage profiles.
• Build predictive analytics pipelines for fault detection and long-term battery health monitoring.
• Collaborate with embedded systems and hardware engineering teams to integrate AI models into physical BMS hardware.
• Optimize AI/ML pipelines for deployment in resource-constrained microcontroller environments.
• Publish results in peer-reviewed journals (IEEE Transactions on Industrial Electronics, Journal of Power Sources, or equivalent) and present at conferences.

Requirements

• PhD in artificial intelligence, machine learning, electrical engineering, control engineering, or a closely related field, completed before the start date.
• Hands-on experience with supervised and unsupervised ML applied to time-series data, including regression, neural networks, and model evaluation.
• Proficiency in Python with experience using scikit-learn, TensorFlow, or PyTorch, and comfort working with battery or sensor time-series datasets.
• Ability to work in a multidisciplinary team; English is the primary working language.
• Willingness to be based on-site at UM6P’s Benguerir campus.

Nice to have

• Prior work on edge AI or embedded ML deployment using TensorFlow Lite, ONNX, or similar runtimes on microcontrollers.
• Experience with battery cycle testing protocols (capacity fade, impedance spectroscopy) or direct collaboration with electrochemists.
• Publications in ML for energy systems, prognostics and health management, or predictive maintenance.
• Familiarity with EV battery pack architectures and BMS hardware interfaces (CAN bus, SPI, I2C).

How to apply

Apply through the Academic Positions portal at the link above. Submit a CV, a research statement of 1 to 2 pages describing your relevant background and interest in the role, and contact details for two academic referees. Shortlisted candidates will be contacted for a video interview. For informal enquiries, contact the Green Tech Institute research office through the UM6P website.