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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
To strengthen our team in the division 7.7 “Modeling and Simulation” in Berlin-Steglitz, starting 01.07.2026, we are looking for a Research Assistant (m/f/d) with a Ph.D. in Civil Engineering
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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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explainability methods into LLM-based pipelines Develop and evaluate prompt engineering strategies (zero-shot, few-shot) Support experimental setup, data generation, and evaluation of model behavior Write, test
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atomistic modelling and / or electronic structure modelling is required. Background knowledge in semiconductor- or nanotechnologies is advantageous. Experience with workflow tools or AI-driven material
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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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systems engineering and product development, preferably with a focus on sustainability and circular economy. In-depth knowledge of materials science, sustainability-driven material selection and/or business
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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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translational or clinically integrated research portfolio aligned with NIH-style priorities, including technology-enabled surgical innovation, oncologic precision medicine, and data-driven surgical decision