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Field
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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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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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the application of molecular dynamics simulations to molecular systems, ideally in both methods and applications. Experience in Atmospheric Water Harvesting, metal organic framework modelling and machine learning
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Associate selected for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins
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Children's Cancer Center. The Postdoctoral Associate will finish experiments, data analysis, and manuscripts related to the IL18 mediated enhancement of CAR NKT anti-tumor activity project. The ideal candidate
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simulations), computational chemistry, machine learning, enhanced sampling methods, force field development, or related fields are encouraged to apply. Start date is flexible. the postdoc will collaborate with
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research should they wish it. Bucknell has numerous opportunities to both learn how to be an effective teacher and to teach courses and labs both in the electrical and computer engineering department and
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for cognitive science and artificial intelligence, including about 35 PhD students. Core research domains include cognitive science, machine learning, deep learning, games, virtual reality, computational
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
, repeatable coverage of fire-prone regions. When combined with modern statistical and machine-learning approaches, these data enable robust mapping of fuels, assessment of burn severity, estimation of biomass