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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | 25 days ago
Engineering, Systems Engineering, Data Science, or a closely related field Experience with artificial intelligence, machine learning, physics-informed neural networks, or reduced-order modeling (ROM
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | 25 days ago
: Experience artificial intelligence, machine learning, digital twin technology, control systems, or computer simulation Point of Contact Cinnamon Eligibility Requirements Degree: Master's Degree or Doctoral
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
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developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and analysis of new genomic
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Computational Postdoctoral Researcher - Bacterial Genomics, Transcriptomics, and Single-Cell Genomic
statistical modeling, machine learning, or other quantitative approaches for genomics data analysis. • Interest in working closely with experimental scientists and contributing to interdisciplinary biological
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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in
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to Principal Investigator level. Research areas include: Population neuroscience and multimodal neuroimaging Computational psychiatry and imaging genetics AI and machine learning for brain science Multi-omics