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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 LLMs
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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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, emissions, and productivity. Decision-Support & MCDA Implement a machine-learning-driven multi-criteria decision analysis to rank and select optimal decarbonization pathways. Collaborate with industry and
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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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(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
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban Health Principles: Familiarity with urban planning principles centered on health (active mobility