63 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Argonne
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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. The selected candidate will develop computational models at the mesoscale and/or macroscale based on the principles of mass, momentum, and energy conservation to describe processes such as morphological change
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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Argonne National Laboratory’s Accelerator Science Division is seeking a Postdoctoral Appointee to contribute to the development of a Sub- THz Collinear Structural Wakefield Accelerator
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of these systems are insufficient to cover the diversity of human-driven experimental activities. The development of multi-appendage, dexterous robots with embodied intelligence is a key to closing this gap
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The Nanocomposite Materials and Membrane Manufacturing group in the Applied Materials Division (AMD) has an opening for a postdoctoral researcher to develop materials, membranes, and fabrication
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Argonne National Laboratory invites applications for a Postdoctoral Researcher position in Superconducting Detector Development and CMB Instrumentation. This role focuses on advancing large arrays
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computational research. They are intrinsically driven, goal-oriented, and can work collaboratively with others. Working closely with the CPS divison, the postdoc will leverage AMReX and the LBM to develop
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that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational