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principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with spectroscopic signatures. Formal requirements include a Master's degree in
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always held a central role in applied mathematics and, nowadays, its popularity has surged in fields traditionally focused on real-world applications, such as engineering, machine learning and imaging
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, sensing at the robot–environment interface, and bioinspired control strategies to allow the robot to perceive and adapt to different terrains. By bridging soft robotics, physical intelligence, and learning
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solutions that are practically viable. The successful PhD candidate is expected to carry out research on one or more of the following focus areas: Developing and training robust machine learning surrogates
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is linked to the new research center FME RenewHydro . You will join the research group Electrical Machines and Electromagnetics (EME) at IEL, where we foster an open, inclusive, and collaborative
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research, develop and apply state-of-the-art optimisation and machine learning methods to problems within ship design, encompassing hull, powertrain and internal designs. The successful candidate will have
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of separating fire-induced signatures from natural environmental variability (weather, canopy changes, tree motion) and fluctuations in the SoO sources themselves. Machine-learning methods will help improve long
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measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning
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outcomes and economic performance, specifically addressing challenges such as overdiagnosis in cancer care. We will utilize economic theory, simulation, economic evaluation and machine learning to quantify
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Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research into this important field