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samples, human subjects/clinical research data and clinical trials. Demonstrated experience in research infrastructure leadership, biobanking governance, data governance, or large-scale biomedical programs
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of the Remote Sensing Lab. ABOUT THE REMOTE SENSING LAB (RSL) The Remote Sensing Lab advances geospatial science through machine learning and AI-driven analysis of multi-scale remote sensing data to tackle
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the School of Computer Science at the University of Sheffield, you will lead the machine learning research on acoustic foundation models for respiratory health. You will work with large-scale real-world
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Monday – Friday Day Shift. Job Summary The person in this position will provide direct patient care, including performing prophylaxis, polishing, periodontal maintenance, scaling and root planning, patient
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performing prophylaxis, polishing, periodontal maintenance, scaling and root planning, patient assessment of hard and soft tissues and periodontal exams. Capture radiographic image. Apply fluoride foam and
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performing prophylaxis, polishing, periodontal maintenance, scaling and root planning, patient assessment of hard and soft tissues and periodontal exams. Capture radiographic image. Apply fluoride foam and
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datasets. Process and manage terabyte-scale inpatient healthcare data, including electronic health records, physiological waveform data, and multi-omic datasets. Support multi-site initiatives and federally
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), established in 2016 as the National University of Singapore’s (NUS) hub for lifelong learning, is both an academic school and a university-wide platform. As a school, SCALE specialises in multi-disciplinary
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artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and managing cardiovascular disease by
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. The combined observation of soil processes with plant functional traits and structure of vegetation captured by remote sensing (e.g. LiDAR, multi- and hyper-spectral imagery) in experiments and at contrasting