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Field
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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of software engineering techniques to ensure interoperability of their modelling and mining ecosystem. This position will run from October 2025 (or shortly after) and it has an expected duration of 24 months. A
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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: PhD in nuclear engineering, applied physics, computational science, or a closely related field; demonstrated experience in Multiphysics modelling, numerical methods for PDEs, and code verification and
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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17.07.2025, Wissenschaftliches Personal The Chair of Software Engineering & AI is searching for a new doctoral research associate (TVL-E13) in Generative Engine Optimization. Research focus
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20th August 2025 Languages English English English The Faculty of Technology, Natural Sciences and Maritime Sciences have a vacancy for a position as PhD Research Fellow in Energy Technology
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optimisation techniques and AI-based models to support decision-making in microgrid design and operation. Working in collaboration with a leading global mission critical firm of engineers, this project offers