14 web-development-"https:"-"https:"-"https:"-"Itä-Suomen-yliopisto" PhD positions at Empa
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thin films for emerging technologies. By combining state of the art physical vapor deposition, high throughput experimentation and data science, we develop thin films with enhanced performance and
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. Empa is a research institution of the ETH Domain. Within our focus research Digital Health Solutions, we develop technologies for continuous long-term monitoring of individuals. At the Biomimetic
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project aims to develop hydroborate-based all-solid-state batteries. A central objective is to achieve a deep, mechanistic understanding of the processes that govern electrochemical performance and cycling
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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
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tasks You will be based at Empa to characterize the physio-chemical properties of plastic particles and will use ice nucleation instruments at ETH Zurich to simulate cloud ice formation processes. Prepare
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. Empa is a research institution of the ETH Domain. The Laboratory Chemical Energy Carriers and Vehicle Systems Laboratory conducts, develops and optimizes processes for renewable fuels. The group
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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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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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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