134 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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, tensor analysis, and network science to foster the professional development of team members. Qualifications and experience essential PhD in Applied Mathematics in the fields of Numerical Linear Algebra
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embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource-constrained environments, including edge AI applications. Guide PhD students and
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-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. All UM6P programs run as start-ups
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values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker
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Eucalyptus plantations. Forest Ecology and Management, 494, 119275. Cornut I. et al. (2022a) Potassium-limitation of forest productivity, part 1: A mechanistic model simulating the effects of potassium
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emissions modeling. Simulation & Optimization Framework Build and validate a dynamic simulation model of mine haulage operations. Integrate multi-objective optimization to explore trade-offs across cost
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collection and scenario development. Techno-Economic & Environmental Assessment Model life-cycle costs and greenhouse-gas emissions for IPCC, trolley-assist, battery-electric trucks, etc. Perform sensitivity
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experimental results. Ability to work in interdisciplinary teams that involve experimentalists and modelers. Criteria of candidates: PhD in Chemical/Mechanical Engineering, Energy, Applied Chemistry, Applied
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their thermophysical properties. Proficiency in computational fluid dynamics (CFD) simulation of thermal energy storage systems, enabling the modeling and analysis of heat transfer, fluid flow, and thermodynamic
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. Contribute to the supervision of master and PhD students. Qualifications: Ph.D. in Earth Sciences, Remote Sensing, Physics, Applied mathematics, or related field. Strong background in land surface modeling