107 software-formal-method-phd Postdoctoral research jobs at MOHAMMED VI POLYTECHNIC UNIVERSITY
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: The candidate must have: PHD with skills in one of the following fields: history, philosophy, anthropology, sociology, psychology Rigor / motivation, Good communication skills, Teamwork An appropriate scientific
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dynamic) using simulation software such as Aspen Plus, gPROMS, or COMSOL Multiphysics. Solid understanding of process control strategies, including model predictive control (MPC), nonlinear control, and
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: The Postdoctoral researcher will be expected to: Publish in high impact journals in the field. Participate to the supervision of PhD students and research internships. Criteria of the candidate: PhD in the field
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publish in leading international journals. Desired skills and experiences: PhD in crop physiology, microbiology, plant molecular physiology, or related fields. Strong knowledge about plant-soil-microbes
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design and very good programming skills (Python, Pytorch). Excellent command of IT tools and perfect command of AI and Data Science tools. Profile required: PhD in computer science, specializing in AI and
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UM6P standards. Candidate Profile: PhD in plant ecology, environmental sciences, or a related field. Experience in phytomanagement, plant ecology and biology, revegetation of degraded sites. Knowledge
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. Write scientific reports and publications, and present results in project meetings and international conferences. Desired profile The position is intended for a PhD holder in environmental engineering
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modalities. Therefore, the ideal candidate will be expected to conduct homologous overexpression, gene silencing, sequence analysis and contained phenotyping. Education A PhD in Molecular Biology, Genetics
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transduction, proteomics, drug design, or related fields Good knowledge of cell biological and molecular biological methods Experience in bioinformatics tools related to proteomics and drug design. Aptitude
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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