82 evolution "https:" "https:" "https:" "CMU Portugal Program FCT" PhD positions in Switzerland
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for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2025/phd-positions-in-high-voltage-… Requirements Research FieldEngineeringYears of Research
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https://academicpositions.com/ad/eth-zurich/2025/phd-position-spatio-temporal-c… Requirements Research FieldComputer scienceYears of Research Experience1 - 4 Research FieldEconomicsYears of Research
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to apply Website https://academicpositions.com/ad/eth-zurich/2025/phd-position-in-energy-efficie… Requirements Research FieldComputer scienceYears of Research Experience1 - 4 Research FieldEngineeringYears
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or equivalent LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number of offers available1Company/InstituteARTORG CenterCountrySwitzerlandGeofield Contact City Bern Website https
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CenterCountrySwitzerlandGeofield Contact City Bern Website https://www.unibe.ch Street Freiburgstrasse 3 Postal Code 3010 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo Gmail
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hidden links in the dark proteome. At the SIB Swiss Institute of Bioinformatics, we develop SWISS-MODEL (https://swissmodel.expasy.org ), a widely used web-based platform for generating high-accuracy 3D
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Your position A fully funded PhD position is available in the Computational Pharmacy group at the University of Basel. The successful candidate will contribute to ongoing research on the development
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development of new sensors, support nanoparticle-based cellular reprogramming strategies and identify new omics-based biomarkers. We work closely with clinical partners and we focus on deep understanding
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strong support for personal and professional development. The PhD students will be enrolled in the ETH Zürich doctoral program. The position is available from April 1st 2026 or upon agreement. We live a
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive