position: Ben Geria
Position type: Postdoctoral research
Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa, aiming to position itself at a world-renowned university in its field.
The university is engaged in economic and human development, and places research and innovation at the forefront of Africa's development. The mechanism that enables Morocco's forefront position in these areas is a unique partnership-based approach and boost skills training related to the future of Africa.
Located in Bengeril city, in the heart of Green City, Mohammed VI Polytechnic University aims to retain its mark nationwide, continental and globally.
The Green Tech Institute at Mohammed VI Institute of Technology (UM6P) in Bengerir, Morocco is looking for highly motivated postdoctoral researchers to join their teams to explore the intersection of energy transition, circular economy and sustainable construction production. The purpose of this position is to promote innovative solutions that promote sustainability in a built environment, focusing on effective integration of renewable energy, resource efficiency and waste reduction. Candidates must complete their PhD in urban or rural development, civil engineering, or in related fields. Candidates are expected to have practical experience in fields related to urban or rural planning, renewable energy, and sustainable construction.
Location Summary:
We are looking for talented and motivated postdoc researchers to join cutting-edge teams, working on developing advanced AI/ML algorithms for Battery Management Systems (BMS) in electric and micromobility applications. The main focus is on the creation and optimization of charging (SOC) and cutting-edge predictive models to ensure the safety, efficiency and lifespan of lithium iron phosphate (LFP) batteries.
Main responsibility:
- Develop and implement machine learning algorithms for SOC and SOH estimation.
- Analyze large datasets from the battery system to improve model accuracy and performance.
- We will conduct research on predictive analytics for real-time battery health monitoring and fault detection.
- Work with the embedded systems and hardware engineering team to integrate AI models into the BMS.
- Optimize your AI/ML pipelines for resource-constrained environments, including edge AI applications.
- Guide doctoral students and collaborate with engineers to implement advanced ideas.
- Presenting research findings at author's scientific publications and conferences, contributing to patents and technological innovation.
Qualifications:
- PhD in artificial intelligence, machine learning, data science, electrical engineering or related fields.
- Powerful experience developing and applying AI/ML models to energy systems or similar applications.
- Proficiency in machine learning frameworks (e.g. Tensorflow, Pytorch, Scikit-Learn).
- Practical expertise in programming languages such as Python, R, Matlab.
- A solid understanding of battery systems, electrochemical processes, or energy management.
Priority Skills:
- Experience in time series analysis, predictive modeling, and anomaly detection.
- Knowledge in real-time applications of AI/ML on embedded or IoT devices.
- Knowledge of cloud-based computing platforms for data processing (e.g. AWS, Google Cloud).
Understanding BMS architecture and electric mobility systems.
