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) Country Switzerland Application Deadline 24 Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 41 Is the job funded through the EU Research Framework Programme? Not funded
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bioinformatician to work on omics data. Project background The Laboratory of Regenerative and Muscle Biology (RMB), headed by Prof. Ori Bar-Nur, is a multidisciplinary and collaborative group of scientists focused
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. These positions will contribute to a broader research program focused on devising pathways toward a just and sustainable biosphere by examining how governance systems, digital technologies, and socioeconomic
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broader research agenda focused on how digital infrastructure and computational tools can support (or hinder) progress toward effective, coordinated, and equitable biodiversity governance. Research topics
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of computational and statistical genomics, and bioinformatics. Cattle are an interesting «model organism» to study inherited genetic variation and the molecular-genetic underpinnings of complex traits and dieseases
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, for preclinical testing of engineered circuits where relevant; collaborating closely with molecular biologists, computational modelers, and clinical and preclinical research partners; documenting results clearly
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science, engineering, computer science, and medicine to find bold, creative solutions to real-world problems. Prof. Menon’s lab is located in the Balgrist Campus , designed to foster collaboration. Built in
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these positions should be directed to Prof. Christoph Müller muelchri@ethz.ch or Dr. Paula Abdala: abdalap@ethz.ch . About ETH Zürich ETH Zurich is one of the world’s leading universities specialising in science
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applications from prospective PhD students who are interested in performing research in Mathematical and Theoretical High Energy Physics. The PhD work will be carried out in the group of Prof. Alba Grassi
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, advanced materials, and innovative concrete strengthening solutions. Our work combines experimental investigations, analytical modelling, and numerical simulations to develop and validate novel reinforcement