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Field
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved
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assessing starch efficiency. The project will provide valuable insights into improving starch functionality, by linking raw material properties to paper performance. Additionally, the study will explore
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create world-class research results through collaboration between researchers, students, and public and private companies. This is how we achieve insights, new solutions to societal problems, and knowledge
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analytical tools such as small-angle X-ray scattering (SAXS) and fluorescence cross-correlation spectroscopy (FCCS). Our goal is to bring quantitative insights into how soft-matter systems self-assemble and
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of enzyme activity, stability, kinetics, and structure–function relationships. The goal is to gain fundamental insights into enzymatic degradation — knowledge that can ultimately guide the development of more
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, to industrial catalysis and green energy production. This PhD project offers a rare opportunity to explore hydrogen surface collisions at a fundamental, molecular level to gain unprecedented insight into the role
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simulations to analyze and optimize hemodynamic parameters in CAD-related applications. Collaborate with clinicians to translate simulation insights into practical solutions for cardiovascular health. Develop
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particularly aligned with HTC Health’s long-standing commitment to evidence-based nutritional solutions for human health and is informed by industry insights into weight management and consumer-driven demand
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gaining deep insights, we aim to design catalysts that fully exploit alkali species, driving real progress toward a more sustainable world. As part of our team, with expert supervisors in catalyst design
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model the plastic deformation of solid solutions of tungsten carbide in a cobalt binder under nanoindentation, including MD-based hardness predictions and defect analyses, to gain fundamental insights