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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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live together with their real-world counterparts. This project aims to identify better how perishable biological products, such as vaccines, react inside cold chain unit operations and to pinpoint why
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applications in forecasting, system optimization, flexibility management, and resilience analysis. The work will be carried out in close collaboration with our interdisciplinary teams at both Empa and EPFL, as
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deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting, system optimization, flexibility management, and resilience analysis. The work will be
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-quantitative methods for decision support for sustainable innovation. Your tasks With the PhD work, we plan to adapt and advance the SSbD framework in the area of green electronics, with a special focus on bio
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characterization. The experimental nature of the work requires a very high level of experimental skills, an analytical mindset for interpreting results, and the ability to design innovative experiments. You are a
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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Electronics Your tasks With the PhD work, we plan to adapt and advance the SSbD framework in the area of green electronics, with a special focus on bio-based materials and an application in agriculture, health
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scientific work with CFD. Interest in working in interdisciplinary teams. Good knowledge of the German and English language. Our offer Interesting and responsible tasks in the field of applied research
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new generation of catalytic nanomaterials for water treatment, made of Earth-abundant elements and designed for their long-term performance. To fabricate and make these materials durable, the core