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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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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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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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, 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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. As such, activity-driven changes in synapse nanostructure can thus drive adaptive changes in synaptic strength. This project aims to resolve how heterogeneity in molecular nanostructure shapes
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the production of spatially granular and engineering driven representations of model results. Experience working across disciplines in multi-institution and multi-stakeholder collaborations is desirable
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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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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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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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of Composites for Energy and Mobility Laboratory (Composites Lab) is located at King Abdullah University of Science and Technology and is part of the Physical Science and Engineering Division. The Composites Lab