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Field
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science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
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for downstream applications such as mapping, simulation, analyses and other related uses. This PhD investigates methods to transform raw 3D data into structured scene representations that integrate
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fault detection into better maintenance decisions and more reliable operations. You will work in an applied research environment with railway-sector partners. Your immediate leader will be the Head of
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more reliable operations. You will work in an applied research environment with railway-sector partners. Your immediate leader will be the Head of Department. About the project TrainGate uses trackside
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analyse fast transient or pulsed electrical signals using appropriate measurement instrumentation. Select appropriate measurement bandwidths, probes, cabling and acquisition methods for reliable high
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to research in porous and photoactive materials. You will first implement new reliable and quantitative diffusion-ordered optical spectroscopy that will permit rapid and automated determination of fundamental
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responsible for the HVT group’s laboratories, ensuring reliable and safe operation Support and guide PhD candidates and MSc students in planning and conducting experimental work Contribute to the design
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tuition fees and a tax-free stipend. Available to Home fee-status applicants only due to funding structure. Delivered in collaboration with Intel, including industrial co-supervision. The student will work
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BAM Bundesanstalt für Materialforschung und -prüfung | Berlin, Berlin | Germany | about 2 months ago
the reliability of non-destructive testing and structural health monitoring methods The objective is to develop and validate a hybrid approach combining simulation and experiment to analyse the reliability
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data availability, and the need for reliable performance guarantees. In this project, you will investigate how the strengths of modern machine learning can be combined with the rigorous foundations