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dedicated researcher with a PhD in mathematics, electrical or mechanical engineering, computer science or a related field, and a strong methodological background in machine learning. The ideal candidate has
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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of scientific articles in peer-reviewed journals Presentation of research results at international conferences and scientific meetings Your profile PhD in Physics, Materials Science, Electrical Engineering
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of cooperative effects in charge transport, proton dynamics and electrochemical conversion in solids and at electrochemical interfaces. Your tasks The research focuses on experimental investigations and theory
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the electrolyte exchange charge with solid booster materials held in the storage tanks, decoupling energy density from active-material solubility while preserving the modularity and safety of aqueous flow batteries
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datasets with multi-omics profiles of tumors from previous studies Developing new machine learning models through collaboration with the Swiss Data Science Centre Establishing data analysis pipelines
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full support in advancing the current project as well as in shaping and realizing your own scientific ideas within the lab's research activities. You will be encouraged and supported in producing high
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, or related discipline. As this project is a cutting-edge research activity beyond the current state-of-the-art in electrocatalysis, we are looking for someone creative, curious, highly motivated, and with
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: Characterization of sorbent/porous materials characterization e.g. nitrogen sorption (BET, BJH, NLDFT), breakthrough measurements, SEM, TEM, TGA, FTIR, NMR, thermal conductivity measurements Synthesis and/or shaping