333 evolution "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" "Sciences Po" positions in Switzerland
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100%, Basel, fixed-term The Computational Evolution Group, led by Prof. Dr. Tanja Stadler, in the Department of Biosystems Science and Engineering (D-BSSE) at ETH Zürich works at the interface
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With a service-oriented and improvement-driven mindset, you and the team will contribute to the continuous development of innovative, data-driven approaches and projects in the context of open data and
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We are seeking to hire an outstanding colleague with demonstrated potential for research leadership in international law, especially in the areas of international human rights law and international humanitarian law. Candidates must hold a doctoral degree in law or an equivalent academic...
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plenty of possibilities for personal and professional development. Fully funded four-year PhD position at Empa in Dübendorf, starting from September 2026. Joint supervision/research resources and
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Pharma Research and Early Development (pRED). The program focuses on developing new tools and methods to address challenges in medicine and therapeutic development. Project background Adeno-associated
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Zurich or another Swiss university Proven experience in software development Strong Python programming skills, with demonstrable experience in several of the following areas: GUI development CUDA for GPU
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as well as stable isotopes in tree rings. Forest sites are located in Switzerland and Czech Republic. Job description Your main tasks will include: Development of knowledge-guided machine learning
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building material, contributing to the sustainable development of the built environment. Project background This PhD position is part of the interdisciplinary based at the Chair Gramazio Kohler Research
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innovative methods to leverage machine learning for numerical weather forecasting and climate modeling. Project background We are looking for a motivated Machine Learning Scientist to join the development team
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to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would be across the spectrum from methodological development