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: Alexandre José Malheiro Bernardino (ist13761) Organic Unit: Scientific Area of Systems, Decision and Control Scholarship Theme: Computational Auditory System Simulators and Machine Learning-based Optimisation
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technologies, such as low-power long-range (LoRa) and high-throughput, low-latency technologies (5G). In the context of machine learning, communications play a central role in data sharing and in the decision
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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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will focus on developing efficient foundation models to medical image analysis. Foundation models offer a scalable and adaptable solution for medical image analysis by learning generalizable
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engineering? Then this professor position might be for you. We are looking for a new professor to lead research in probabilistic machine learning, with a focus on areas such as deep generative models, Bayesian
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required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, led by Laura Cantini, works at
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 1 day ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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03:59 AM UnitedKingdomTime) Position Description: Apply Position Description The Fuqua School of Business at Duke University invites applications for the position of Adjunct Professor to teach
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models