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Field
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not yet competitive for 5-year clinician scientist fellowships. This post is designed for applicants with a research interest in machine learning or data science approaches for patient stratification
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, and formulation of clinical study design, image processing, machine learning, and statistical analyses to illuminate specific research questions. Among the machine learning techniques, deep learning
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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, specifically in Computer Vision Proficiency in python and deep learning libraries (pytorch, Tensorflow) Preferred Qualifications: Experience in Medical Imaging (ideally in Ophthalmology) Track record
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to develop analysis pipelines for image data and knowledge of developing machine learning experiments with common challenges (e.g. noisy data, imbalanced datasets, missing data, etc.) Experience in conducting
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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have experience with applications of machine learning and deep learning on medical image data that you have experience applying methods within generative artificial intelligence to medical images and
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equivalent. Strong background in machine learning and computer vision. Prior experience in data-efficient classification, synthesis, and detection is preferable. Strong publication records in top-tier machine
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-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology. Digital pathology images contain rich
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* Experience with image analysis or medical imaging Established record of peer-reviewed scientific publication Prior experience / comfort with computer programming is a plus Modes of Work Positions that