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department at TU/e for finite element-based deformable body simulations. Conduct research on mechanical contact processing models, integrating both physics-based numerical models and data-driven approaches
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Integrated photonics is maturing and integration density increases. This means the design methods and testing strategies need to keep up. Are you interested in exploring compact models and novel
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to neighborhood outcomes. In close collaboration with local stakeholders, experiments will be conducted to test model predictions and interventions. During the PhD, you will: Gain expertise in theory-driven
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-driven probabilistic risk assessment models and inspection planning balances cost of inspection and risk of failures, and accounts for reliability and safety. Furthermore, inspection decisions and safety
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events, will be used to enhance the diagnostic and predictive capabilities of the safety level for the individual lift. Combining data-driven probabilistic risk assessment models and inspection planning
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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. The project approach includes: Employ a novel data-driven approach to accurately model the energy consumption and production of Smart Cities, on the level of city blocks and individual buildings. Identify and
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experimental chemistry. Additional training in a wide range of soft and hard skills is available at the University. What you will do: This PhD project aims to bring together multi-scale modelling and data-driven
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Engineering Description Key areas of research include: Scenario generation with LLMs to expose hidden failure modes in E2E models. Embedding regulatory, behavioral, and environmental knowledge into generated