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Field
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
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learning, foundation models, including large language models, agentic AI, multi-agent AI systems, transfer learning, self-supervised learning, and federated learning. The Postdoctoral Researcher will be
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, knowledge-driven models and AI-based decision support can be integrated to support resilient and energy-aware manufacturing systems. Special emphasis will be placed on multi-objective optimization, learning
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dynamics and agent-based modeling approaches. Demonstrated ability to communicate complex technical findings to both scientific and applied audiences. Proven success mentoring undergraduate or graduate
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on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks and system
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PostDocs on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks
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that seeks to foster the development of skilled manpower and natural resources in Morocco and Africa. UM6P has robust teaching and research programs with a base of cutting-edge research facilities and
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agents. The Postdoctoral Associate will work as part of a highly integrated research team utilizing a variety of laboratory technologies and approaches. Specialized skills in molecular/cellular biology and
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knowledge of developing AI-based information systems, such as AI-enabled engagement e.g., agentic AI and AI-enabled insight generation e.g., machine learning analytics in both individual and organizational
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supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based