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network, including 4 training schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing
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steady and transient state, at scales ranging from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with
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of Architecture and the Built Environment), where you will collaborate closely with a parallel PhD project within the Faculty of Aerospace Engineering focused on meshfree numerical methods. Together, you will work
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quantitative image analysis, numerical modeling, and explainable AI (XAI) with state-of-the-art biophysical methods. Using techniques such as traction force microscopy, microfluidics, 3D bioprinting, and
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Computational Fluid Dynamics. Operational skills : Physical analysis of fluid dynamics, advanced skills in programming and numerical methods, writing scientific reports and articles, presenting at scientific
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sound, with human speech at the pinnacle of complexity. Like human babies, songbirds learn their vocalizations early in life from a social tutor. Numerous parallels to human speech learning, including
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of the research engineer is to set up multi-physics simulations with large supercomputers on the perimeters of the project by developing advanced pre- and post-processing, the necessary models and numerical methods
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from the physics and mechanics of the underlying multi-phase microstructure. An integrated numerical-experimental approach is generally adopted for this goal. A state-of-the-art computing infrastructure
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Deep Learning Computer Vision Edge AI, TinyML, and Embedded AI Explainable AI Safe AI Federated, Parallel & Distributed, Computing/Learning Control Systems Optimization Planning and Scheduling Human
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity