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Field
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large
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design, including ground improvement techniques e.g. Deep Mixing or piled embankments. Experience working in industry-academic partnerships, especially related to high-speed rail. Proficiency in numerical
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SD-25157 RESEARCHER IN ATMOSPHERIC PLASMA TREATMENT OF METALLIC SURFACES FOR INDUSTRIAL APPLICATIONS
of metallic surfaces. The researcher will work closely with the industrial partner to build up a deep understanding on the mechanisms driving the surface modification and develop strategies to implement
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highly stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key
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, reinforcement learning with human feedback (RLHF), and agent-based orchestration for analyzing unstructured operational data. The position further encompasses several problems related to AI for Operations, where
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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qualified in relevant subject area (or nearing completion) Good knowledge of computer vision algorithms for image and video processing Strong experience in machine learning or deep learning for medical image
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of biosystems and for extracting knowledge from (vast) sets of biotech data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and
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related field Demonstrated experience in interdisciplinary research and excellent digital literacy Strong interest in historical data, machine learning, data visualization, or digital hermeneutics Strong
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rules which enable effective learning in large and deep networks and is consistent with biological data on learning in the cortex. In particular, the research will focus on evaluating and extending a