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testing of prosthetics/exoskeleton to developing new methods to investigate neural control of movements. The group is renowned for its pioneering work in musculoskeletal simulation, leading to the creation
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Job Description Would you like to become part of the solution toward green energy transition, by helping develop the methods for modeling future power and energy systems? The Renewable Plants in
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understanding learning processes from a cognitive, embodied, and/or epistemic perspective familiarity with design-based, qualitative, or mixed-methods educational research a solid foundation in digital research
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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the Computer Science study program. The stipend is open for appointment from August 1st 2025 or soon thereafter. The PhD students will be working on topics within the general areas of formal methods, model checking and
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qualifications we are looking for: Excellent knowledge and practical experience on current molecular microbiology methods Experience with genomic and transcriptomics data analysis is beneficial. Experience with
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-driven methods to characterize demand-side flexibility using e.g., by using a set of coupled stochastics differential equations or data-driven digital twins. As a result, we will aim at obtaining a
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-situ spectroscopic and microscopic methods, including XRD, Raman spectroscopy, TEM, and XPS. Evaluating catalytic performance for various electrochemical reactions, such as the oxygen reduction reaction
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challenges and decision-making under uncertainty. Ability to translate conceptual models to their mathematical formulation and to test them with numerical and simulation experiments. Excellent communication in
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September 1, 2025, or as soon as possible thereafter. The f -elements of the periodic table play an essential role in numerous technologies, including catalysts, magnets, and photonic devices. Their distinct