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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed
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to numerous preclinical research projects focused on the development of novel molecular magnetic resonance imaging (MRI)-based techniques for early detection, disease phenotyping and monitoring treatment
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 28 days ago
PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
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scientists with numerous international collaborations and partnerships and have funding from the National Institutes of Health and the Canadian Institutes of Health Research. This position will join a diverse
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Associate. This postdoctoral researcher will work under the supervision of Prof. Bo Ji and Prof. Lingjia Liu, conducting research on immersive communication/computation-aware optimization of next-generation
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and optimization of measurement-based quantum computing protocols for quantum simulation of quantum many-body models. Preference will be given to candidates familiar with the stabilizer formalism and