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is on energy modeling, data-driven planning approaches, and the development of innovative data infrastructures for national and municipal energy planning. Candidates should have experience in energy
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for magnetic particle transport in fluid flows and validating the results through a laboratory-scale reactor. It combines numerical modeling, AI methods, and hands-on experimental work in the context of advanced
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formal methods. The successful candidate will contribute to advancing data-driven modeling techniques that enable formal safety guarantees for complex dynamical systems such as autonomous vehicles
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of secure control systems. DES provide a natural framework to model the logical and event-driven dynamics of systems such as production systems, supply chains, and transportation networks. The goal
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applications for a joint professorship under the Jülich Modell with the Hasso-Plattner-Institut für Digital Engineering gGmbH (HPI) to be filled as soon as possible: Full Professorship (W 3) for Software Testing
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-edge solutions that enhance the explainability of AI models, develop models for relevant applications, and improve learning efficiency. Become a part of our team and join us on our journey of research
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on optimal sequential decision-making in critical healthcare. The project aims to develop mathematical foundations for rigorous, interpretable, and data-driven decision models that integrate: Markov Decision
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change Identify, evaluate, and prioritize market opportunities, and analyze customer needs and market potential in the field of Explainable AI (XAI) Develop viable business models, value propositions, MVP
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Associate Professor of Data-Driven Material Modeling (salary group W2
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dynamics subproject, which develops hybrid and data-driven approaches with a strong focus on improving the representation and generalization of ML models for the dynamics of Ecosystem-Atmosphere Carbon and