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, or related background. Strong background in machine learning, computer vision, and deep learning. Knowledge of transformer architectures and foundation models. Experience with few-shot learning, self
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of the following: Experience with Explainable AI. Experience with Deep Learning. An interdisciplinary background / interdisciplinary training. Have followed courses in Psychology or Philosophy
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machine-learning approaches (e.g. UMAP). Investigate the effects of deep brain stimulation on speech production in relation to individual connectivity profiles. Coordinate closely with clinical
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, mathematical logic or statistical learning theory. For PhD position 2, we appreciate prior experience in implementing deep learning models for graphs and networks. Our offer As a PhD candidate at UT, you will be
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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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research profile, and an international network around big data in marine sciences. The candidate will have access to NIOZ’s high-performance computing cluster, GPU nodes for deep learning, dedicated data
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: Roadmapping optical technology developments and the introduction of new capabilities for space missions, from low to high technology readiness levels; Introducing deep-space optical communication into space
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, we appreciate prior experience in implementing deep learning models for graphs and networks. Additional Information Benefits As a PhD candidate at UT, you will be appointed to a full-time position for
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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equator to pole, from the continental shelf to the deep ocean and from the past to the present. The ocean is Earth’s largest reservoir of CO2 and heat; circulation, mixing, biogeochemistry and other marine