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proposals, scientific publications, statistical and project reports Your profile: University degree (PhD) or any other equivalent combination of education in epidemiology related sciences, including
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macromolecular dynamics with machine learning, statistical mechanics, molecular simulations, and experimental data. The joint project “FAIME – Flexible and Efficient AI-driven Molecular Simulation Engine” is part
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. The data obtained will be subjected to statistical analysis and prepared for scientific publication. The project will be carried out within an international team of scientists. Your Profile: Completed
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investigates how data from learning environments can support agency and social networks in higher education and workplace training. The group is part of the TUM School of Social Sciences and Technology, the
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | about 2 months ago
. The work is embedded in an interdisciplinary environment at the interface of statistics, data science, and clinical epidemiology. Research Objectives The main objectives of the PhD project are to: Develop
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Degree Doctoral degree, Dr rer nat Course location München In cooperation with Technical University Munich, Helmholtz Center Munich, Max-Planck Institute for Biochemistry Munich Teaching language
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• Proficiency in statistical or programming tools (e.g., R, Python) • Interest in education and learning as an application domain • Ability to work independently • Demonstrated academic writing
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The Leibniz-Institute for Educational Trajectories invites applications for the following full-time position (salary according to the collective agreement "TV-L" max. E13, 100% working time
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the intersection of explainable artificial intelligence (XAI) and causal inference. Our goal is to develop AI systems that are causally understandable, such that predictions can be interpreted in terms of underlying