19 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Babes Bolyai University" PhD 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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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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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