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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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analyze how these factors influence vote choice, and how the role of quality in elections can be strengthened. Empirically, the project combines surveys, survey experiments, and text-as-data methods.
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two hours per week (during the semester). Master Degree in Political Science or related areas Skills in quantitative methods and/or computational methods (e.g. text-as-data methods) Strong command of R
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German) Contribute to the activities at the Political Science department Your profile Master's degree in political science or related discipline Skills in quantitative methods and/or computational methods
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Skills in quantitative methods and/or computational methods Strong command of R Didactic competence and enthusiasm for teaching Willingness to collaborate with colleagues, and to contribute to the academic
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Your position The candidate will have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical
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security, privacy-preserving, and efficient computational methods to AI for genetic healthcare. Start: April 2026. Duration: 4 years (48 months).
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Behaviour in Turbulent Fluids through Analytical and Probabilistic Methods” (grant number 233216). The successful candidate will investigate both phenomenological and theoretical aspects of turbulent fluids
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their arguments; and which moral framings make constructive dialogue more likely within the context of new plant breeding technologies. Using a mixed-methods approach (qualitative, quantitative, and experimental
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these factors influence vote choice, and how the role of quality in elections can be strengthened. Empirically, the project combines surveys, survey experiments, and text-as-data methods. Master in Political