358 process-engineering-"https:"-"https:"-"https:"-"https:"-"https:" positions in Switzerland
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than
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no fixed deadline, starting date can be any time, the sooner the better. ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent
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artificial intelligence. ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct
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in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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in Chemistry, Materials Science, Chemical Engineering, Process Engineering, or a closely related field We are looking for a highly motivated, curious, and self-driven researcher who enjoys tackling
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. Document data workflows, standards and project procedures. Collaborate with software engineers and researchers to improve data management processes. Profile Required qualifications MSc Bioinformatics
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EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than
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characterizing individual nanoclusters • Engineering and purify protein nanopores with tailored sensitivity to size, charge, and etc. • Developing data analysis pipelines and machine learning approaches for signal