64 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY in Morocco
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch
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Sensing, or related field Experience in atmospheric modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record
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The successful candidate will contribute to the following research axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate
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, Hydrology, Environmental Science, or a related field. Experience in machine learning or AI applications in hydro-climate studies. Strong background with GIS tools and spatial analysis techniques. Demonstrated
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industrial conditions. Keywords: Artificial intelligence, autonomy, digital twin, edge computing, UAV systems. Objectives: Support the development of AI and machine learning algorithms for autonomous
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, reports and projects. Oral and written communication skills. Computer skills/programming/modeling would be a plus. Candidate Criteria Ph.D. in Analytical Chemistry or a related field. Extensive experience
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development