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What if you could design systems that not only follow instructions — but understand intent and guarantee correct behavior over time? We are looking for up to two PhD students who want to explore
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML
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that utilize streets and roads to manage stormwater, there is limited knowledge on effective design and implementation strategies. This PhD project aims to investigate how urban roads can be designed and
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-scale GAC filter data to aid in system understanding, design, and optimisation. Main responsibilities Conduct research within the PhD project (>80%) Publish scientific articles and present your work
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safety. You will work on developing control algorithms all the way to performance assessment in test vehicles. The project combines theoretical aspects of control algorithms, experimental design, and
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Looking for your next challenge? Become a part of a team that’s driving change and innovation every day. This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research
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training systems. Furthermore, we aim to develop a measurement framework to assess the impact of such attacks on training performance, adversary costs, and model accuracy. Our ultimate goal is to design a
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domain knowledge and trained models on similar problems can be both incorporated to train better models and guide researchers to design better experiments. The project will be in collaboration with other
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-fabrication processes for superconducting devices Automatic bring-up and calibration of quantum processors Design and simulation of quantum processors Optimal-control techniques for high-fidelity qubit
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consensus mechanisms, and designing scalable defense strategies that uphold privacy and security guarantees. The candidate will work at the intersection of systems, networking, and security, contributing both