4,762 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 20 days ago
surface characterization techniques. The development of automated pipelines for image-based model construction, registration across measurement modalities, and propagation of data processing uncertainties
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is expected to contribute to foundational research in dynamic modeling, stability analysis, and control of coupled data center–grid systems. Emphasis will be placed on developing new theoretical
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for molecular systems and thin films in combination with AI-driven process optimization. Help us shape the future of chemical research! Self-driving lab platform for photochemical and porosity research Join a
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quantum materials and van der Waals devices with the development of THz microscopy and precision spectroscopy, while exploring their translation into quantum information science. Postdoctoral Scholars will
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Science and Artificial Intelligence. The lab models, simulates, and analyzes social phenomena using computational, experimental, and theoretical methods. We pursue consequential questions
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
that could eventually be used in the clinic. The Postdoctoral Research Associate will develop diagnostic and prognostic deep learning models for Alzheimer’s disease, using rich existing multimodal datasets
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underlying juvenile dermatomyositis and related pediatric conditions. Through the integration of advanced molecular techniques, patient-derived samples, and functional muscle modeling, you will help develop
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. Laura Blecha. Preference will be given to candidates with experience and/or interest in modeling gravitational-wave or multi-messenger source populations, the cosmological evolution of black holes and
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to develop predictive models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials
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development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow