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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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factors assessment). You should hold a relevant PhD/DPhil (or near completion) and have publications in medical image analysis or computer vision video analysis. Knowledge of ultrasound imaging is not a
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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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weighing up to 25 pounds; and sitting at a computer workstation for extended periods of time; and occasionally working at heights above four feet and/or in confined spaces. Also involves regular
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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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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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, organizing, reviewing, and preparing student records for disclosure. May assist with identifying and applying record redactions where required under FERPA, etc. ● Review and process duplicate identity and
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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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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
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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