40 cloud-computing-"https:" "https:" "https:" "https:" "https:" "https:" "St" "University of St" positions at Empa
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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collaboration with PD Dr. Isabel Hostettler (HOCH Health Ostschweiz). The candidate will perform research at Empa in St. Gallen with joint affiliation to ETH Zürich (D-HEST, Prof. Peter Wick). Desired starting
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. Empa is a research institution of the ETH Domain. Our Laboratory for Advanced Fibers in St. Gallen develops functional polymer fibers for medical and technical applications. Together with partners
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continuum modeling (finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and
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We provide an international and stimulating research environment with excellent infrastructure for personal and professional development. This PhD project will be carried out at Empa St. Gallen under
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Laboratory is located in St. Gallen at the Swiss Federal Laboratories for Materials Science and Technology (Empa). Our team is highly interdisciplinary and international, providing an inspiring environment
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postdoctoral researcher who is eager to contribute to the translation of fundamental research into clinically relevant antimicrobial solutions. Our offer The Biointerfaces Laboratory is located in St. Gallen
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excellent opportunities to establish connections in the local industry. The work will be carried out at Empa St. Gallen under supervision by Dr. Gordon Herwig and Prof. René Rossi. The position is temporary
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the potential extension by one additional year) at Empa St. Gallen in the Bio-Nano Assembly research group, which is part of the Center for X-ray Analytics, the Laboratory for Biomimetic Membranes and Textiles
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore