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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its mission is to
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on research projects spanning evaluation of deep learning neural networks trained on signed language recognition. The fellow will work closely with the PI Annemarie Kocab and collaborator Alex Lu , Senior
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and qualifications A PhD degree in computational biology, machine learning, computer science, data science, bioinformatics, or a related discipline Demonstrated machine learning experience in form
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Karlsruhe Institute of Technology - Institute of Applied Geosciences - Division of Geothermal Research | Karlsruhe, Baden W rttemberg | Germany | 3 months ago
learning and deep learning workflows for automatic signal classification, including supervised, unsupervised, and/or semi-supervised (hybrid) approaches to use both labeled and unlabeled data. Model
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Lexington Avenue, NY, NY Categories: Education/Teaching Position Summary: The Katz School of Science and Health at Yeshiva University invites applications for an Adjunct Faculty member to teach PhD
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and other generative systems). The ideal candidate brings a strong machine learning foundation, curiosity about sound and music computing, and enthusiasm for collaborating with PhD students and postdocs
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explanation of each output’s impact. Teaching statement (up to 1 page) addressing your approach to applied learning Where to apply Website https://www.timeshighereducation.com/unijobs/listing/413184/assistant