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workstation for extended periods of time and performing tasks with repetitive motions (such as typing). Also involves some risk of injury from moving, lifting or positioning patients, equipment, or materials
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management systems. Preferred Qualifications Experience supporting cardiovascular clinical research studies. Familiarity with image review processes, study protocol development, and sonographer training
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therapeutic targets. Develops and applies machine learning and statistical modeling approaches for biomarker discovery, classification, prediction, and patient stratification. Develops algorithms and
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relevant instruction in diagnostic imaging, facilitate case-based and interactive learning and assess student performance. The faculty member will work collaboratively with the Department of Medical
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algorithms for computer vision, image processing, imaging, and/or physics-informed reconstruction. Scientific track record with publications at internationally renowned venues in machine learning. Solid
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research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to innovative melanoma and skin cancer research
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-image pipeline producing around thirty machine images, a self-hosted monitoring platform, a centralized security-logging pipeline, and in-house compliance and security automation — all managed as code in
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, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data, how it can be extracted, and how
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processing, machine learning, computational imaging, and clinical translation. Beyond your individual research contributions, you will serve as a technical coach for the PhD researchers, helping to align
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intelligence and machine learning across several domains of physics and astronomy. The successful candidates will develop an integrated image-processing pipeline and apply it to three distinct problems: rare