15 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Universitat de Vic (UVIC UCC)" scholarships at Empa
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) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and equitable urban energy systems. Our work combines technology and policy with systems thinking and
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. The group Multiomics for Healthcare materials at Empa, St. Gallen generates and integrates multi-modal biomedical datasets with the aim to inform development of new sensors, support nanoparticle-based
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. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2025/phd-position-on-antimicrobial-materi… Requirements Research FieldChemistryYears
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field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision of students Your
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Opportunity to work in a network of 15 PhD students in parallel Opportunities to present work at scientific conferences and to publish in high quality journals Personal and professional development support You
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. Please submit these exclusively via our job portal. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2025/phd-position-in-data-driven
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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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. Empa is a research institution of the ETH Domain. The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and
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development of electrochemical sensors detecting environmental pollutants, providing real-time information for effective management. Past and current work includes electrochemical sensors for airborne virus
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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