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closely related field. Required Skills : Proficiency in Python/C and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or Keras). Interest in or prior experience with
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for Spatial Biology Description: The Lu Lab at Stanford University is seeking a postdoctoral fellow with deep expertise in advanced AI and generative modeling to develop computational frameworks that transform
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(e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection. Advance algorithms for multi-modal tomography (X-ray, neutron, electron
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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required minimum?: No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY27 minimum
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
to, multimodal+embodied semantics, human-like language generation and Q&A/dialogue, and interpretable and generalizable deep learning. The duties of the postdoctoral scholar will include aiding the research
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 19 days ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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contributing to multiple projects including resilience-aware scheduling, deep learning workload job scheduling, and storage system performance tuning. The candidate will have the opportunity to engage in
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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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19.07.2022, Academic staff The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current focus on deep