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                in solution for up to hours. Now, we leverage the unique abilities of nanopore trapping to detect proteins and their conformations, dynamics, interactions, catalytic mechanisms etc. in real-time 
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                how perishable biological products, such as vaccines, react inside cold chain unit operations and to pinpoint why some products decay faster. For that purpose, we develop digital twins of the cargo 
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                eager to work in an inter - and transdisciplinary setting. One PhD position will be focusing more on consumer choice and demand-driven food value-chains and policy aspects while the other PhD position is 
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                are causing major changes in the physical environment, which may in turn fundamentally alter the functioning of ecosystems. Alpine and arctic ecosystems have remained relatively natural so far, but 
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                , teaching and administrative tasks. The position is funded for 4 years with an initial contract for 1 year. At the end of the first year, an assessment based on performance and a detailed research proposal is 
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                some products decay faster. For that purpose, we develop digital twins of the cargo, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end 
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                will develop and apply advanced methodologies, including scenario analysis and the innovative use of satellite data, to model the exposure and vulnerability of companies to climate-related hazards (e.g 
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                supportive scientific environment. As a PhD student in this project, you will: Conduct quantum sensing experiments at cryogenic temperatures Investigate the photophysics and spin dynamics of NV and SiV centers 
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                applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff 
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                reasoning domains. This work will blend cutting-edge experimentation - spanning RL, few-shot learning, meta-learning, etc. - with formal analysis to push the boundaries of what modern AI systems can reliably