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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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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
. Empa is a research institution of the ETH Domain. The Laboratory for Building Energy Materials and Components develops advanced and/or low eco-impact, porous materials for insulation, sorption, and
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employment conditions, and strong support for personal and professional development. The PhD student will be enrolled in the ETH Zürich / University of Zürich doctoral program , depending on academic
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are explored for applications ranging from magnetoelectronic devices to advanced biomedical systems. A central strength of our group is the development and operation of unique, home-built scanning probe
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for applications ranging from magnetoelectronic devices to advanced biomedical systems. A central strength of our group is the development and operation of unique, home-built scanning probe microscopy platforms
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) Close collaboration with micromagnetic modeling work (Prof. Dieter Suess, University of Vienna) Close collaboration on MPI and MPS measurements (Prof. Dr. Matthias Graeser, University of Rostock) Close
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
develops advanced and/or low eco-impact, porous materials for insulation, sorption, and energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property
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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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tasks You will work on projects related to the future of sustainable asphalt pavements, including binder aging reduction, development of alternative binders, developing bitumen emulsions, and working
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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