74 scholarship-phd-agent-based-modelling Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 17091 Overview: The Environmental Sciences Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Earth Systems Modeling Group (ESMG
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multiple institutions to collaboratively support scientific advances guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major
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Requisition Id 17142 Overview: We are seeking a Postdoctoral Research Associate who will focus on urban-scale energy modeling. This position resides in the Grid Interactive Control Group in
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Requisition Id 17092 Overview: The Environmental Sciences Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Earth Systems Modeling Group (ESMG
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: Develop physics-based, data-driven, and hybrid (physics-informed ML) models of thermal systems to capture dynamic thermal behavior Validate models against experimental data and refine model accuracy and
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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well as dynamic and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self
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. You will perform cutting-edge research on theory and modeling of dynamics in condensed matters. Major Duties/Responsibilities: Development of theoretical framework for driven and open quantum systems
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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systems (proteins, enzymes, membranes, and complexes) Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity Contribute to cross-scale modeling