263 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Brunel-University-London" positions in Switzerland
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project “eDIAMOND: Efficient Distributed Intelligent Applications in Mobile-Network Dynamics” . The eDIAMOND project aims at developing new methods and systems for decentralized and distributed data-driven
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aspects of nuclear safely. The focus of the project is to gather essential data on the liquid source term, improve our understanding of the associated phenomena and develop tools to simulate the relevant
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systems. The wide range of experiments conducted in the BedrettoLab relies on numerous sensors and data acquisition systems that monitor processes both in the tunnel and within the surrounding rock mass
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have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance
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, engineering, physics, or a related field, and with strong interest in the cryosphere. The successful candidate has experience in computational data analysis or numerical modelling. You are eager to work
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process The review of applications will begin on 23 February until the position is filled. Further information about the Planetary Geochemistry group can be found here . Questions regarding the position
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to magnitude -5 at meter-scale distances. All data is acquired and processed through SeisComP. The BedrettoLab team comprises about 30 scientists from diverse disciplines and collaborates with research
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for projects that show unconventional thinking and introduce a unique approach. The focus is on promising ideas of high originality for which preliminary data are not necessarily available (high-risk research
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well as close interactions with theoretical groups during the data analysis and interpretation. Your profile You have completed a PhD degree in solid state physics or materials science You have an excellent
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, data-driven frameworks that enable robust monitoring and condition assessment of infrastructure fleets. By combining smart sensing with distributed intelligence and advanced stochastic modelling