La Trobe University

Job Title: Postdoctoral Researcher – AI/ML for Energy Systems Location: Ben Guerir Position Type: Postdoctoral Research Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields. The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa. Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally. The Green Tech Institute at Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco, is seeking a highly motivated postdoctoral researcher to join our team in exploring the intersection of energy transition, circular economy, and sustainable construction production. This position aims to advance innovative solutions that promote sustainability in the built environment, focusing on the effective integration of renewable energy, resource efficiency, and waste reduction. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected to have hands-on experience in field related to urban or rural planning, renewable energy and sustainable construction. 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.

Title: Post-doc position Discipline: Process Mineralogy, Mineral Processing, Artificial Intelligence Duration:12 months, with the possibility of renewal for an additional 12 months Institutions: UM6P-Benguerir/ Mineral-X - Stanford University Starting date: As soon as possible We are seeking a Postdoctoral Fellow to join an exciting research project related to improving the recovery of phosphate from flotation tailings using process mineralogy. The successful candidate will have the opportunity to lead and conduct research at the intersection of process mineralogy, mineral processing, and artificial intelligence in a collaboration between Geology and Sustainable Mining Institute (UM6P, Morocco), and Mineral-X (Stanford University, USA).  Qualifications PhD in process mineralogy, mineral processing, mining, chemical or geological engineering, or a PhD in a related field with mining/mineral processing experience. Knowledge of process mineralogy, froth flotation through past research, coursework, or job experience. Proficiency in programming (Python, Julia) (provide evidence with specific examples). Experience with statistical modelling and experimental design. Ability to work in a multidisciplinary team. Strong written and oral communication skills. Context and objectives Process mineralogy is a discipline that has recently been used for diagnosing and improving ore processing flowsheets such as flotation. It is well known that flotation performance depends on several intrinsic and extrinsic parameters. The intrinsic parameters of the process include, among others, the type of reagents, their dosages, the solid concentration, and the air flow rate. However, the extrinsic parameters include the properties of the ore to be floated, namely its particle size distribution, the degree of mineral liberation, mineral associations, and the surface properties of the minerals. In general, a mineral cannot be floated even if the processing conditions are optimized if it is locked (encapsulated). To improve flotation recovery, regardless of process parameters, it is essential to conduct a physical, chemical, and mineralogical analysis of the tailings to study the distribution of phosphates and their relationships with gangue minerals such as silicates and carbonates. Main tasks The selected candidate will work on: Process-mineralogical characterization of the feed, concentrate, and flotation tailings. Testing the effectiveness of different grinding energies on phosphate liberation within flotation tailings. Developing an AI-driven intelligent framework to optimize grinding time to enhance phosphate liberation with minimum slime generation. Design of laboratory flotation tests to optimize process parameters. How to apply? Candidates must upload the following documents via the recruitment platform: Cover letter summarizing your research interests and qualifications. CV including a list of publications and research projects. Contact information for 1 reference.

Postdoctoral fellow Position in Omics and Soil Microbiome Research (2 years) The Mohammed VI Polytechnic University (UM6P) invites applications for a Postdoctoral fellow position to join an interdisciplinary research project focused on metaproteomic analysis of soil microbial communities. This project aims to identify key genes and enzymes involved in mitigating salinity stress and improving soil health and crop productivity. These positions are part of a broader initiative to develop biofertilizers and sustainable agricultural strategies to combat soil degradation and salinization. Research will involve state-of-the-art metaproteomics, bioinformatics, and functional microbiome studies conducted in field trials, greenhouse experiments, and laboratory settings. This role will focus on the interactions between rhizosphere microbiota and plant roots, investigating how microbial enzymes enhance plant stress tolerance under salinity conditions. The successful candidate will: Conduct metaproteomic and genomic sequencing of microbial communities associated with plant roots. Identify microbial enzymes and functional pathways involved in nutrient acquisition and stress adaptation. Analyze plant-microbe interactions in response to salinity stress. Collaborate on biofertilizer development incorporating beneficial microbial enzymes. Required Qualifications: Ph.D. in Microbiology, Bioinformatics, Soil Science, Plant Science, Environmental Science, or a related field. Expertise in microbiome data analysis, metagenomics, or metaproteomics. Proficiency in bioinformatics tools for multi-omics data processing Strong publication record in relevant fields. Ability to work in a multidisciplinary team and collaborate across research groups. Preferred Qualifications: Experience in functional characterization of microbial enzymes. Familiarity with microbial ecology and soil biochemistry in stress adaptation. Background in field and greenhouse experimental design. Application Process: Applicants should submit the following documents: Cover letter detailing research interests and relevant experience. Curriculum Vitae (CV) with a full list of publications. Contact information for three references. For inquiries, please contact achraf.elalali@um6p.ma