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-world environments inevitably face noisy data, distribution shifts, and situations their training never anticipated. Existing machine learning research focuses on limiting the impact of such disturbances
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of settings where only parts of a system are extreme. Special attention will be given to parametric families like the Hüsler–Reiss distribution, which lead to an extremal analogue of Gaussian structural causal
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of photonic technologies into future wireless infrastructures. You will investigate how photonic integrated circuits can enable scalable and energy-efficient signal generation, beamforming, distribution, and
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limited by unknown material characteristics such as molecular weight distribution, branching, and compositional heterogeneity. This project aims to develop a physically grounded framework that links
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far from straightforward. To illustrate, the benefits of urban greenspaces are typically not evenly distributed among urban populations, often amplified by historical disinvestment in marginalised
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analyses of adaptation effectiveness, avoided economic losses and damages, and distributional impacts across society. By applying household survey data from different European cases, we can advance the
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count, area distribution, inter-cluster distance) to cellular Potts model (CPM) simulations of spheroid growth and drug responses. Integrating multi-modal data streams, such as live-cell imaging, bulk and
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of Electrical Engineering, Mathematics and Computer Science (EEMCS), an internationally recognized research group in wireless communications, embedded systems, and distributed intelligence. Depending on your
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compared to "classical" on-body sensors - design techniques for intra-oral medical sensors are much more stringent than for on-body sensors. For reference please read the following article https://dl.acm.org
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grids. The group is currently active in applications such as Direct Current distribution and transmission grids and microgrids, electrification of mobility (charging of electric vehicles), and renewable