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Foundation Health Model. As a PhD candidate, you will conduct deep-dive research into training pipelines and reasoning techniques for clinical foundation models. You will join an elite, interdisciplinary team
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optimization algorithms for complex trial design spaces. Required qualifications: A PhD or equivalent doctoral degree in biostatistics, statistics, applied mathematics, operations research, computer science
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 16 days ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). The forthcoming Atmosphere Observing System (AOS) mission aims to provide spaceborne observations
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codes and process their results. Helping to develop new models and algorithms to simulate pulse propagation, the material response, and other aspects of our experiments. Coding in Julia and python. Take a
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. Duties/responsibilities Performing high level theoretical, and numerical research and data analysis (55%) Presentation publication, and proposal preparation (20%) Mentoring PhD and MSME students (10
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, use imitation learning algorithms to learn pick-and-place actions, design HRI experiments with users, evaluate data, and share the code and benchmarks in open repositories. This postdoctoral position is
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the project. What you will do Conduct original and high-quality research in machine learning and computer vision; Develop novel algorithms for adapting and specialising visual foundation models; Publish
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and experiments with modelling of these experiments. As part of this, we have developed new algorithms and a completely new web-based platform – EasyNMR - for performing such modelling/simulations. With
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applications for postdoctoral positions, to be hosted by one of the faculty members listed below. Candidates must hold a PhD in mathematics, or expect to be awarded one by the end of the calendar year of
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; Develop novel algorithms for adapting and specialising visual foundation models; Publish research findings at leading machine learning and computer vision venues such as CVPR, ICCV, ECCV, NeurIPS, and ICLR