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biomarkers. To develop organ-on-a-chip to evaluate health impact of environmental pollutants. To perform data analysis and result communication. Profile A doctoral degree in biotechnology, bioengineering
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observations, and to quantify how fires contribute to the global ozone background now and in the future. Job description The doctoral student will develop skills in atmospheric chemistry, global modeling, data
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should be directed to Prof. Dr. Daniel J. Müller, daniel.mueller@bsse.ethz.ch and/or Prof. Dr. Sascha Fauser, sascha.fauser@roche.com (no applications). Further information about the Biophysics Group
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professional and personal growth. Your responsibilities will include designing and conducting wet-lab experiments that address specific research questions, analyzing complex data and effectively communicating
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technologies (i.e. computing, data, algorithms, AI) in society. We are in particular looking for candidates who have interest and experience with STS and humanities pedagogy in the context of a technical
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for interviews and exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. Further information about the ETH Phenomics
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interpretation of beamline data Development structure-based multiphysics models to study (electro-)chemo-mechanical phenomena in Li-Ion batteries You will have the opportunity to use and build upon extensive
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at our discretion, based on factors such as application volume and suitability. Further information about the ETH Student Project House and our machines can be found on our website or on Instagram . About
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information about the ETH Student Project House and our machines can be found on our website or on Instagram . About ETH Zürich ETH Zurich is one of the world’s leading universities specialising in science and
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analytical and molecular techniques Profile MSc degree in biomedical engineering or related discipline Previous experience in tissue engineering and cell culture is required Previous experience in data