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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep
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cabling issues. Preferred Skills: Experience in higher education, preferably in areas with technology in teaching, learning, and/or administrative systems. Experience creating technical solutions using
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including electrical/electronic engineering or similar. Experience in federated learning/deep reinforcement learning is preferred. At King’s, you will join a research-leading and multi-disciplinary team led
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on lncRNA structure. These experimental constraints will then be used to guide deep learning-assisted RNA 3D structure prediction tools, in order to generate ensembles of structural models. Clustering and
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
) Collaborative Context and Expertise Since 2018, EDF has been studying the construction of meta-models based on deep neural networks (i.e., deep learning). Initially, based on fluid simulations using Code_Saturne
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University of New South Wales | Canberra, Australian Capital Territory | Australia | about 6 hours ago
cryptography or applied cryptography. A strong math background in related areas such as coding theory will also be considered. Capability of applying deep learning models and willingness to use them
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excellence as inextricably linked. Our faculty combine high-impact scholarship and outstanding teaching with a deep commitment to inclusive excellence and we expect successful candidates to show a record of
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competitions. Excellent communication skills in English; Chinese language skills are a plus Industry experience preferred Must have a research background in Artificial Intelligence, Machine Learning, or Deep
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research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation/geohyd and https://www.mn.uio.no/geo/english/research/groups/remotesensing ). We are a growing, lively