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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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therapeutics, wearables, artificial intelligence (AI) and machine learning (ML), public health surveillance systems, and virtual/augmented/extended reality. Health conditions of interest are also broad and may
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. Essential qualifications and experience a PhD (or near completion) in one of the following fields (or a closely related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or
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, bioinformatics, biomedical data modeling and ontologies, biomedical natural language processing and information retrieval, health artificial intelligence and machine learning, privacy technology, global health
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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with expertise and experience in (1) Generative Modeling, (2) Multimodal Learning, (3) Generative AI and Computer Music, and (4) Efficient AI. Duties The appointees will be required to: (a) conduct
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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-state model will be approximated using machine-learning surrogates and will be used for a real-time optimization, such that the plant operates optimally despite disturbances. The candidate will be part of
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in the United States. Preferred methodological skills include statistical analysis of survey and other large-n data, qualitative interviews, and/or text analysis and machine learning skills