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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation
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of the following areas: Image processing, partialdifferential equations, scientific computing, deep learning.•Solid programing skills. Knowledge of Python, Tensorflow/Pytorch, and Mathematica.•Fluent
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of Orthopaedics and Sports Orthopaedics and the Institute for AI and Informatics in Medicine. We work at the intersection of artificial intelligence, medical imaging, and clinical practice, developing methods
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, segmentation of ultrasound images, identification of intraplaque hemorrhage on MRIs, post-processing by ultrasound spectroscopy and imaging by ultrasound localization. You will join a dynamic team specialized in
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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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(CHX, 11-ID) beamline at NSLS-II seeks a postdoctoral researcher with a strong background in computational imaging, data science, or scientific computing. This position will focus on developing advanced
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. Strong background in neuroimaging techniques (e.g., MRI, PET) and computational modeling. Proficiency in programming languages such as Python, MATLAB, or R. Experience with image processing tools and
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engineered animal models, single-cell profiling, and advanced image processing and analysis. In addition, numerous institutional shared resources and core facilities support the laboratory by providing
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will have connections to both the Molecular Imaging Program at Stanford (MIPS) and the Radiological Sciences Laboratory (RSL). The ideal candidate for this position will have interest in being trained in
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preclinical, 3 T vertical for plant imaging). The recruited researcher will benefit from a stimulating scientific environment at the crossroads of physics, mechanics, and life sciences, in an interdisciplinary