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generation of atomic structures and training of machine learning interatomic potentials (MLIPs) in a way that maximizes the capability of high-performance computing (HPC) platforms available in the Oak
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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. This
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machine learning techniques to improve DER/IBR model calibration, parameter estimation, uncertainty assessment, and decision support. Build and apply hardware-in-the-loop test platforms to validate the full
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a Machine Learning Research Scientist who will support the development of fundamentals for GeoAI imagery processing, self-supervised learning methods for large vision-language models to enable
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specializing in artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine
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discovery. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially
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distributed computing, machine learning, and architecture search, you will use leadership-class and exascale systems to evaluate and optimize new approaches. You will collaborate across disciplines, help
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industrial automation, machine learning, mobile robotics, process control, sensor processing, machine vision, and/or human machine interaction. This position will require working with external partners
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, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and
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their usage in advanced machine learning algorithms. This position resides in the Manufacturing Systems Analytics (MSA) Group in the Manufacturing Science Division (MSD), Energy Science and Technology