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motivated to advance trustworthy artificial intelligence in medical imaging? In this postdoctoral position, you will work on explainable AI and foundation models for CT imaging, combining methodological
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research facilities, and high-performance computing resources. Education Requirement: - MBBS, MBBCh, MS, MD, PhD, or MD/PhD in Radiology, Medical Physics, Biomedical Engineering, Computer Science, or a
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and reliable quantification in theragnostic studies, strengthening the translational relevance of imaging data. Close collaboration with medical physicists within the unit will support quantitative
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publications in medical image analysis or computer vision video analysis. Knowledge of ultrasound imaging is not a requirement but an interest in research at the interface of machine learning with real-world
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of Biostatistics at Brown University is seeking a highly motivated individual for a postdoctoral research associate position interested in developing statistical methods for modeling medical imaging data
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evaluating computational methods, analysing imaging data, collaborating with clinicians for real-world impact, and contributing to publications. About You PhD (or near completion) in computer science
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photonic imaging and patch-clamp electrophysiology. The postdoctoral fellow will contribute in particular to different aspects: - in vivo calcium imaging (ex vivo imaging approaches and patch-clamp
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video-based CHD biomarkers. You will work closely with clinical domain experts to define and evaluate AI models. You should hold a relevant PhD/DPhil (or near completion) in medical video analysis
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underlying disease progression – and to translate these insights into therapeutic strategies. Our lab combines cutting-edge in vivo imaging, electrophysiology in mouse models and computational modeling with
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Qualifications PhD in Neuroscience or other related field Experience in in vivo dosing (IV,IP, SC), mouse brain stereotactic surgery, in vivo two photon imaging, image analysis Qualified applicants must be