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aims to build predictive and physical binding models of protein – DNA interactions using high-throughput and quantitative biochemical binding data across hundreds of thousands of sequence variants
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. You enjoy combining experimental laboratory work with theoretical analysis and modelling. While your main focus will be the research project and your own development as a researcher, the position also
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introduces new and underexplored vulnerabilities to network-based threats. The goal of this research is to uncover such threats, evaluate their impact on training performance and model integrity, and develop
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Materials Science are found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production - and in the interaction between these areas
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Computational Arts, Music, and Games within the DSAI division. About the research project This position is related to investigating learned cultural representations in data search spaces of generative AI models
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-planning of timber production and nature conservation, two important objectives in forestry. The work involves developing knowledge and tools for habitat modelling through 1) mapping existing habitats, 2
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developing AI methods for automated microstructure analysis and 3D microstructure generation. By combining self-supervised learning and diffusion-based generative models, the goal is to: Reconstruct high
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on the following criteria: Knowledge in electric power engineering, power electronics, and power system analysis Experience in modelling, simulation, and experimental work Proficiency in Swedish and English, both
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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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. The research school for stress response modelling in IceLab Starting in the spring of 2025, Umeå University’s interdisciplinary research hub, IceLab, will offer doctoral positions through the new Stress Response