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. The BlueMat project ?Multiscale Operando Super-Resolution Imaging of Water Imbibition in Hierarchically Porous Materials? aims to establish methods for super-resolution imaging using deep learning. You will
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-Based Wildfire Smoke and Air Quality Monitoring; Deep Learning for Post-Wildfire Damage Assessment. PROFILE of the OFFICE OF POSTDOCTORAL AFFAIRS (OPA) The mission of the UNLV Office of Postdoctoral
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Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques
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development. Research objectives This project proposes developing an integrated digital twin platform that bridges experimental characterisation, multi-physics modelling, and deep learning to enable predictive
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experiential program designed to advance lifelong learning and the education and training of Pharm.D. graduates. A key element of the program's success is preparing Fellows to meet evolving workforce needs
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of statistical analysis is required. Strong programming skills in Python, R and Linux-based software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing
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on. We welcome students, faculty and staff from every background and perspective into a community where everyone feels seen and heard. We have deep-rooted mindfulness for the natural world and all who
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in MRI sequence programming, preferably using the Siemens IDEA platform, is a plus, particularly for projects involving sequence development. Experience applying deep learning techniques
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: https://ucnet.universityofcalifornia.edu/resources/employment-policies-contracts/bargaining-units/postdoctoral-scholars/contract/ Qualifications Basic qualifications (required at time of application) PhD
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 3 months ago
, startups and patient partners. Your Mission as PhD is to develop novel machine-learning methods to (1) detect small, clinically meaningful changes in joints, (2) enable robust longitudinal monitoring and