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well as industrial partners. Furthermore, you meet the following requirements: You hold a Master's degree in Materials Science and Engineering, Mechanical Engineering, Physics, Applied Physics, Metallurgy, or a
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phase change is called desublimation. Although cryogenic carbon capture can produce high-purity CO₂ without relying on chemical solvents, the physics of CO₂ desublimation is still not sufficiently
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of these models therefore remains a key scientific challenge. In this PhD project, you will develop and apply data-fusion methods that combine physics-based wind farm models with wind tunnel and field data. A
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining
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combines microfluidics, bubble physics, and ultrasound signal processing to bring nanobubble imaging closer to clinical use. You will collaborate closely with a fellow PhD candidate, a postdoc, and a
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geoscience, energy systems, materials modelling, fluid dynamics, and other scientific and engineering domains where data-driven models must interact with physical knowledge. Such problems raise fundamental
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Job description The Discrete Mathematics and Optimization group within the Delft Institute of Applied Mathematics at TU Delft is offering a full-time PhD position in the area of combinatorics
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last year, are at a similar stage of their research journey. The current group has a mixed BSc/MSc background, ranging from computer science to physics, electrical engineering and mathematics. Here
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential