16 evolution "https:" "https:" "https:" "https:" "ESPCI Paris PSL" scholarships at Empa
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teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development
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. The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and equitable urban energy systems. Our work combines technology
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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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Membranes and Textiles laboratory, interdisciplinary teams work on the development, integration and validation of novel sensing systems - particularly for textile applications. The focus lies
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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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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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. 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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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