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households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data
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pathways. However, the current common practice is first to build complicated, “black-box” models and try to understand what they learned afterwards. This has the disadvantage that a) interpretation is still
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Integrated optics is currently experiencing unprecedented growth, driven largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing
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. Your research: Develop current-sensor concepts targeting a bandwidth above 300 MHz and an insertion inductance below 1 nH. Model the electromagnetic behaviour, parasitic effects, transfer characteristics
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white matter. The network aims to strengthen the full translational pathway, from understanding disease mechanisms and developing disease models to preclinical therapy testing, clinical readiness and
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implications for competition, food webs, and fisheries. Yet current ecosystem models represent these key grazers only crudely, leaving major uncertainties in future projections. This project asks how changing
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methods that extend current sets of flood scenarios derived from physical and numerical models, incorporate climate change effects, and then use these enriched datasets to assess the future insurability of
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households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data
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? We are looking for a PhD candidate to develop AI methods that extend current sets of flood scenarios derived from physical and numerical models, incorporate climate change effects, and then use
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: Develop current-sensor concepts targeting a bandwidth above 300 MHz and an insertion inductance below 1 nH. Model the electromagnetic behaviour, parasitic effects, transfer characteristics and limitations