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over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation and temperature in
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crucial element tasked with monitoring each cell of the battery and running algorithms to calculate state of charge (SoC), overall health, and more. For instance, in electric vehicles, the BMS carries out
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communications. The candidate must hold (or about to complete) a PhD in the related fields. The candidate will be involved in a two-year research project in collaboration with international academic partners
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candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related fields shown below. The candidate is expected to have hands-on experience in
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website at https://agc.um6p.ma for further details. The ideal candidate must possess a PhD degree in Microbiology, Molecular Biology, Microbial Genomics, Bioinformatics, or an equivalent field. They should
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selected in accordance with this and expected to be in the upper segment of its class with respect to academic credentials. Candidate must hold a PhD in synthetic organic chemistry and must have: Clear
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evaluations carried out in the presence of crops. Required qualifications: The candidates must have strong experience in MOF synthesis and characterization with a PhD and demonstrated experience in Materials
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to grant writing and development of new research proposals. Required Qualifications: PhD in Microbial Genomics, Bioinformatics, Molecular Microbiology, Infectious Disease Biology, or a related field. Proven
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Qualifications PhD in Microbiology, Immunology, Systems Biology, Bioinformatics, or a related discipline. Proven experience in microbiome research, particularly in gut microbiota. Experience with next-generation
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. Ensure timely delivery of project milestones and deliverables. Mentorship and training: Supervise and mentor PhD students and research assistants involved in the project. Provide training on experimental