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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal
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Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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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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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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: Experience applying machine learning methods for predictive analysis. Expereince with the Python programming language. Experience with the creation, validation, and use of synthetic data for constructing
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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dark-field STEM imaging, energy dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy, at the intersection of electron microscopy, software engineering and machine learning. Major