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) 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
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. The primary objective is to design robust and efficient planning solutions—integrated within a digital twin—that account for the uncertainties and variability inherent in industrial processes. Machine learning
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in Operations Research. Strong skills in Machine Learning. Practical experience in mining or industrial environments (through projects, thesis, or postdoctoral work). Ability to work effectively in a
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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Titre: ASARI - Postdoctoral Position in Quantum dots/nanomaterials/Photovoltaics/Solar Energy Entity: Water and Energy department, ASARI. About UM6P: Mohammed VI Polytechnic University (UM6P) is an
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on the development of new easy, accurate, and low-cost tools for soil agricultural soils diagnosis based on the coupling of spectroscopic techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics
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(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease