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AI training has emerged as the standard approach, which utilizes thousands of high-performance devices within a data center to collaboratively process tasks. When they are even more complex, the
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NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science. The focus of this position is on
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public audiences and taking part in a pilot programme where experience can be acquired in high school science teaching; use graph theoretical tools to develop new fundamental frameworks and analytic tools
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Aluminium through Machine Learning, High-Throughput Microanalysis, and Computational Mechanics” - a multidisciplinary research effort at the intersection of machine learning and materials science. This
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activities on fibre-coupled quantum photonic chips in semiconductor silicon carbide, which leads to high-efficiency operation. This will allow you to implement new laser pulse excitation schemes, such as SUPER
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the health and performance of humans in several situations (e.g. firefighters, who are exposed to extreme heat, or older adults who are exposed to heat stress during heat waves) by modeling the environment
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universities. About the research project This PhD project is part of the interdisciplinary WASP-WISE NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through
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University in the Centre for Digital and Design Engineering, part of the Manufacturing, Materials and Design theme. The Centre provides access to advanced simulation, visualisation, and high-performance
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Research Center (CRC) “Data-driven agile planning for responsible mobility” (AgiMo), funded by the German Research Foundation (DFG). This interdisciplinary center, involving four universities and the German
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analyses by age, ethnicity, and index of multiple deprivation will be performed. The second stage of the study will involve the analysis of prospectively collected EQ-5D-5L data from a cohort of patients to