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guidelines for the efficient and automated design and optimization of small-scale turbomachinery. Required competences Strong background in model order reduction techniques, surrogate modeling
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Mission We are seeking a candidate with experience in numerical modeling of nanostructured materials to join our SNSF project “Coupled oscillations in plasmonic crystals”. In this project, we focus
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reinforced concrete and unreinforced masonry buildings. Our approaches include numerical and mechanical modeling, large-scale testing as well as data-driven methods. The PhD candidates will contribute to
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-dependent constitutive models for crack interface mechanics (aggregate interlock) - Large-scale testing of reinforced concrete elements (panels, beams, walls) - Development of analytical and numerical
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Participate in education and supervise student projects More specifically, within this project, you will: Develop and apply multi-objective topology optimization to identify and characterize the
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work on the design, analysis, and implementation of innovative models for decision-making, economics, and operations. Research topics may include optimization, decision-making under uncertainty, market
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and ice nucleating particles Data analysis of observations, numerical model output and remote sensing data Tight collaboration with the project partners and participation in working meetings Teaching
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AI forecasting and optimization tools that enable fair, value-oriented, and grid-friendly energy sharing across consumers, Energy Communities, and multi-community ecosystems. The project goes beyond
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bacteriophages Engineering and characterization of phage-based antimicrobial strategies against pathogenic bacteria and biofilms Characterization and optimization of hydrogel properties and biological
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, process design, energy system optimization, and life cycle assessment, working collaboratively with research and industry partners. You will work in an inspiring, collaborative environment to address