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Learning for Medical Imaging and Multi-Modal Data in Cancer Research Apply for this job See advertisement About the position Position as PhD Research Fellow in Deep Learning available at the Department
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the collective bargaining agreement. The salary range for this position is $84.78 - $109.85 (Hourly Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description A postdoctoral researcher
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connection with the legal adoption of an eligible child, such as travel or court fees, for up to two adoptions in your household. To learn more, please visit: https://www.hr.upenn.edu/PennHR/benefits-pay
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Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g
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. The primary objective is to develop computational methods, using deep learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
-scale neuroimaging datasets. Responsibilities for this position includes: * Developing deep-learning approaches for insight and analysis about mouse behavior from video * Machine Learning: use your
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Regenerative Medicine Postdoc Appointment Term: 1 year initially and renewable. Appointment Start Date: Immediately available but flexible Group or Departmental Website: http://www.liwanglab.org (link is
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++), Deep learning tools (PyTorch) Computer Vision tools (OpenCV, MeTRAbs) English scientific communication skills. LanguagesENGLISHLevelGood Research FieldComputer science Additional Information Benefits
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well as a Ph.D. in Art History or a very closely related field. Typically, they will be expected to teach 6-7 courses per year with the remaining 12.5%-25% of their workload dedicated to service. As