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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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Perform experiments in PSI laboratories and at synchrotron radiation facilities, data processing and analysis Optimization and automation of reaction parameters, correlation of the structural data with
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functionalized lignin as a precursor for carbonaceous structures – Produce optimized ink formulations for laser-induced graphitization processes using functionalized lignin oligomers – Characterize the materials
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concept is the use of plasma technology. Your tasks This PhD position will contribute to the SNSF-funded Ambizione project Plasma4Water . Your main task will be the design, fabrication and optimization
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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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analyses. This project is funded by the Swiss National Science Foundation (SNSF). The PhD candidate will collaborate closely with other PhD students and postdoctoral researchers, both within the group and
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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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be used to address the following questions: What are the mechanisms of embolization of the lung? Can we predict where thrombi end up? What are the optimal drug infusion protocols for efficiently
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position will contribute to the SNSF-funded Ambizione project Plasma4Water. Your main task will be the design, fabrication and optimization of catalytic coatings for water treatment. As a PhD candidate, you
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the most important gas for high-voltage switchgear. Its global warming potential is 23‘500 times higher than that of CO2 and world-wide efforts are underway to develop and optimize equipment with