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advanced machine learning methods for multimodal and 3D medical image analysis in musculoskeletal medicine, in close collaboration with clinicians and computer scientists. PhD or Postdoctoral Researcher
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 6 hours ago
transfer modeling, which leverages in-depth temperature measurements to estimate surface heating conditions. These methods are also invaluable in high-enthalpy test environments like arc jet testing, where
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Treatment. The post-holder will work in the School of Biomedical Engineering & Imaging Sciences, King’s College London, with a team of investigators covering AI, computer vision, robotics, and medical imaging
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/TensorFlow for computer vision tasks Track record of publications in relevant research fields Desirable criteria Multi-modal image analysis expertise Real-time detection and segmentation methods for clinical
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previous five years Minimum Experience: 0-1 year Minimum Field of Expertise: Directly related education in research specialization with advanced knowledge of equipment, procedures and analysis methods
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, so that it can be easily used in practice (fast optimization, embedded decision-making, online updating). 1. Design a lightweight statistical/probabilistic surrogate model, integrating: • an estimation
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psycholinguistics, brain-computer interfaces, robotics, cognitive modeling of language, computational linguistics, educational technologies, computational modeling of evolutionary and adaptive systems, image and
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genetic sequences, from outbreaks and epidemics. These data could be at the household or population level, and the methods development can include causal inference, model diagnostics, estimation
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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data