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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
disorders, and microbiome-related health issues by applying advanced AI/ML techniques for biomarker discovery and metabolic network modeling. Scientific Challenges Addressed in the Position: High
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Positions: two postdoc positions in the development of electrodes materials for ultrahigh performance of metal-ion batteries via advanced multi-scale computational modeling About UM6P: Located
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progression modeling. Explainability and interpretability of AI models to support clinical decision-making and regulatory compliance in healthcare settings. Scalability and computational efficiency in
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digital twins to develop innovative solutions for monitoring, analyzing, and optimizing urban systems in real time. The candidate will contribute to modeling interactions between physical and digital
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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
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Global Critical Zone Science Chair to develop and conduct a research program to better understand forest nutrition and nutritional stress in Eucalyptus forest stands in Brazil. Research context: Forests
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disciplines and create an environment for interdisciplinary research. The research program of ACER is multidisciplinary, with faculty members from backgrounds in Chemistry, Chemical Engineering, and
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: The centre has been recently created to address enduring process challenges in Chemistry and Engineering disciplines and create an environment for interdisciplinary research. The research program of ACER is
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research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and
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Responsibilities: Develop and improve emission inventories for Morocco. Perform source apportionment analysis using statistical and computational techniques (e.g., receptor models, chemical transport models