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, optimization, and the experimental validation of communication-aware robotic systems. Position Requirements: Ph.D. in Robotics, Electrical Engineering, Telecommunications, Computer Engineering, Control
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related to staff position within a Research Infrastructure? No Offer Description SUSMAT - Postdoctoral Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials About UM6P: Mohammed
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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
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focused on Artificial Intelligence (AI)-driven retrosynthesis and reaction prediction. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
, including molecular dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and passion for research in the field
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and
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to the Jorf Lasfar Supply Chain Mohammed VI Polytechnic University (https://www.um6p.ma/en ) is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned
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predictive maintenance in chemical plants. Key Responsibilities: Create and implement hybrid AI models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks