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Field
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Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g
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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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performance models. This position resides in the Materials Engineering Group in the Large-Scale Structures Section, Neutron Scattering Division, Neutron Sciences Directorate at Oak Ridge National Laboratory
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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part of the Physical Science and Engineering Division. The Composites Lab started at KAUST in 2009 and is an integrated environment for composite science, combining modeling and experimental expertise in
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Systems Engineering (Health Systems Data Analysis, Modeling, Computing, and Cloud-Based Health Systems Analytics and Decision Support Platforms) as part of the CTECH Postdoctoral Fellows program. This
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, particularly by providing expert advice in AI/ML-driven materials chemistry. This post will suit an ambitious individual motivated to produce step-change advances in new materials discovery and synthesis by
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feasibility studies. You will also integrate operational feedback from the market-driven dispatch models into financial, regulatory and policy-oriented tools You will contribute to investment and business
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topics, and developing optimization models and/or data-driven controls that are related to at least one of the following topics: Developing energy management strategies related to ADMS, DERMS, microgrid
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About the role We are seeking an outstanding candidate to work in the Process Dynamics group to contribute to advanced research in AI-driven X-ray imaging for metal alloy solidification