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Field
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diseases, but is frequently misunderstood, forgotten, and missed. As a toxic proteinopathy that leads to progressive fibrosis, it offers a powerful model for studying common pathways in CKD and represents a
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· developong new genetic algorithms related to finite element (FE) modeling · presentation of results and their disseminatio The successful candidates must be enrolled in PhD School program
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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are linked to research on composite hydrogen tanks, composite propellers for drones and finite element modelling of textile manufacturing. All research will be conducted with leading companies in
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transfer and laser-matter interactions. The “design-manufacture-inspect-model-test” approach of this project will equip the successful PhD candidate with a wide range of valuable and transferable skills
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in AI: Generative Diffusion & 3D/4D Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Semantic-Aware Compression & Network Information Theory
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of quick clays. A novel combination of miniaturised thermal-hydro-mechanical experiments and particle level modelling will be pursued to unravel the unique mechanisms that make quick clays so hazardous and
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and