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Field
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transition depends on data that almost no one is allowed to see. Distribution system operators (DSOs), municipalities and energy communities need high-resolution grid and consumption data to plan grid
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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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, extensive computing resources and close day-to-day supervision within the group. Tasks and responsibilities: include developing reconstruction algorithms for the muon bundles recorded by KM3NeT; extracting
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depend closely on sea ice and under‑ice habitats, while salps thrive in warmer, often ice‑free waters. Warming and sea‑ice loss are already shifting their distributions and increasing spatial overlap, with
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: include developing reconstruction algorithms for the muon bundles recorded by KM3NeT; extracting air-shower observables such as muon multiplicity, lateral separation and energy; using these observables
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with advanced photoreactors, modular LED light sources, inline analytics, and machine-learning algorithms for the autonomous optimization of photocatalytic reactions. Research will address key challenges
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, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms, memory technologies, and neuromorphic hardware architectures for future
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distribution (QKD), where you will adapt both the codes and their decoders to the specific security and performance requirements of QKD protocols. Beyond QKD, you are encouraged to explore open problems in
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transition depends on data that almost no one is allowed to see. Distribution system operators (DSOs), municipalities and energy communities need high-resolution grid and consumption data to plan grid