118 machine-learning-phd Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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rigor in research studies. Support if required, the development of proposals for research funding. Candidate PROFILE: PhD in Organic Chemistry, Polymer Science, Materials Science, or related subject
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manuscripts, reports, and presentations, with a focus on communicating research findings in the context of thermal energy storage. Criteria of the candidates: PhD in Chemistry, Physics, Materials Science, or
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reports, and showcasing proficiency in scientific communication. Criteria of the candidates: PhD in Chemistry, Physics, Materials Science, or related disciplines, with a focus on inorganic materials and/or
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is also mandatory. Candidate Profile: Hold a PhD in catalytic processes or an equivalent in a relevant field Possess relevant experience in CO2 hydrogenation Demonstrate experience in gas-phase product
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researchers, external labs, and industrial partners. Candidate Profile: Required qualifications : PhD in Mineral Processing, Mining Engineering, Georesources Engineering, or related field. Proven experience in
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communication. Criteria of the candidates: PhD in Chemistry, Physics, Materials Science, or related disciplines, with a focus on inorganic materials and/or electrochemistry. Strong motivation and passion
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the context of thermal energy storage. Criteria of the candidates: PhD in Chemistry, Physics, Materials Science, or related disciplines, with a focus on inorganic materials and/or electrochemistry. Strong
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About Mohammed VI Polytechnic University (UM6P) Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system
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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