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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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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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confrontation avec des inventaires de terrain réalisés par le doctorant et des équipes d'experts au niveau européen, ainsi qu'avec des prédictions issues de modèles d'IA (deep learning et grands modèles de
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | about 2 months ago
bioinformatics or molecular biology position. We're looking for someone genuinely excited by the intersection of structural/molecular biology, physics-based simulation, and machine learning/deep learning – a
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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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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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University of New South Wales | Canberra, Australian Capital Territory | Australia | about 17 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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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