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Electronique, Energie, Automatique (EEA) ou équivalent. Le candidat doit posséder un bon niveau en mathématique et des connaissances en traitement du signal. Des connaissances en machine learning/deep learning
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The University of North Carolina at Asheville | Asheville, North Carolina | United States | about 13 hours ago
student‑centered teaching, our deep commitment to undergraduate research and creative inquiry, and our emphasis on interdisciplinary learning. As a community, we strive to cultivate an environment where
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genomic factors associated with increasing age, and vulnerability for neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease and others. For a deep dive into the effects of aging
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will be contributing to the BESSER
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. The successful candidate will pursue an active research agenda and contribute to the Climate Policy Lab?s ongoing projects, as well as contribute deep expertise in either China, Latin America, or system dynamics
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, we believe science can achieve its fullest potential. THE ROLE During your internship you will work on a projectin the Cultural Heritage Technologies (https://www.iit.it/it/web/cultural-heritage
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(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 maximise
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(PyTorch, scientific Python) with solid experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
more advanced concepts such as tools for supervised/ unsupervised learning that will be helpful for deep learning focused courses. Estimated course enrolment: 35 Estimated TA support: 1 Class schedule