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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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: Enhancing full-scale power-speed assessment reliability by using IoT and big data management Summary This PhD research focuses on uncertainty challenges caused by various disturbances (wind, wave, current
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. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning. Your tasks: Development and comparison of data driven models for the prediction of stresses in
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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collaborate with experts in computational mechanics and machine learning. How to apply ?: Please submit a detailed CV, at least 2 recommendation letters or contact information of people who can recommend you, a
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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-theory-based and recently proposed Moiré Plane Wave Expansion approaches. A significant part of the project is focusing on the development of novel machine learning protocols and workflows based on a large
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computer vision, where the PhD project was fully or substantially method-focused on computer vision and/or AI-based image or video analysis have very strong knowledge of machine learning, with practical
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position