35 evolution "https:" "https:" "https:" "https:" "https:" "University of St" positions at Empa
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. The group is well established internationally in polymer additive chemistry. A more recent focus of the group is the development of sustainable polymer and additives. To strengthen activities in this area, we
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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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list), copies of your academic diplomas and qualifications, a vision statement for your research group (1 page max.). Where to apply Website https://academicpositions.com/ad/empa/2025/2-tenure-track
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technology development, starting from novel materials, deep understanding of the background physics of beam-matter interactions, development of automated AI/ML systems for novel materials processing and
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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/postdoctoral-position-in-atomic-scal… Requirements
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polymer additive chemistry. A more recent focus of the group is the development of sustainable polymer and additives. To strengthen activities in this area, we investigate development of functional covalent
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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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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