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expected to have: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as
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rigorous evaluation methodology for interpretability claims, i.e., faithfulness, stability, and causal grounding. Research, design, implement, and apply advanced machine learning methods for multiple
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. Bioinformatics analysis of data will include use of population genomics techniques, phylogenetics and machine-learning development. The successful candidate will be expected to write up results as manuscripts
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simulations and machine learning for the development of functional material. Assisting the PI to work on the research project and initiate the new projects and to also provide training assistance for the
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models, causal inference and machine learning. Dr. Liu's research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a
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, quantitative imaging, machine learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector
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Description We have an opening for a Postdoctoral Researcher to contribute to experimental heavy-ion physics, detector development, and scientific machine learning within Lawrence Livermore National
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 19 hours ago
learning using 3D data. More information about the research group and KAUST can be found under the following links: http://peterwonka.net/ https://cemse.kaust.edu.sa/vcc https://www.kaust.edu.sa/en If you
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cyclones, the Madden–Julian Oscillation, and atmospheric rivers. We combine theory, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics
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across molecular, cellular, and tissue scales. Research areas of interest include biological imaging and spectroscopy; statistical and Bayesian inference; machine learning for biological discovery