41 modeling-and-simulation-post-doc PhD positions at Delft University of Technology (TU Delft)
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of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty
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for levitated systems — from gas sensing to probing physics beyond the Standard Model. You will join a diverse, motivated, and supportive team of academic staff and students in Delft. We foster an inspiring and
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fidelity simulators. In addition, current autonomous greenhouse control systems focus almost entirely on the highest level of decision making, while ignoring the lower control layers that actually implement
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meter with a non-insulating pipe wall. The research will use existing MHD direct numerical simulations to investigate the flow physics. You will also construct a prototype to test the performance using
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designing and fabricating nanophotonic structures, building an optical low-temperature setup, to performing experiments and simulating spin dynamics. Next to this, you will have the opportunity to build
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the experimental setup, analyze data, and gain experience in modeling, coding, and running complex equipment in our state-of-the-art laboratories. You will also receive comprehensive training to support your
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the potential to advance the development of multi-sensory interactive technologies and the innovative design of immersive systems, as well as the modeling of intelligent adaptive behavior, thereby affecting
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are looking for an outstanding and enthusiastic PhD candidate who has expertise and/or interest in modelling and design of electrical machines and drives, with an MSc degree related to this areas: electrical
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, enabling breakthroughs such as memory-enhanced quantum communication, entanglement-based quantum networks, long-term quantum information storage, and complex quantum simulations. While these demonstrations
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development