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understanding of district heating and cooling, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong skills in programming, modelling, and data
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skills in programming, modelling, and data analysis. Experience in formulating and solving mathematical optimization problems, as well as working on real-world demonstrators, is an asset. Proficiency in
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programming, modelling, and data analysis skills. Experience with formulating and solving mathematical optimization problems is an asset. Proficiency in English is required; good comprehension and oral skills
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, potential toxicity and ubiquitous abundance in the environment. Understanding and evaluating their emission pathways is essential to plan and implement effective measures to reduce emission of PFASs. In
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laser processing and to bring your ideas in AI/ML to the technology level. You have a solid background in programming (deep learning, reinforcement learning, etc.), electronics, high-speed data
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scanning probe microscopy (STM or AFM), ideally with a focus on magnetic nanostructures or spin-resonance techniques Programming skills in Python or similar languages A proactive and collaborative mindset
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. Good knowledge of English (oral and written) is essential. Experience in materials research is important, experience in nanofabrication, micromechanical testing, and numerical simulations is desirable
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programming, modelling and data analysis skills in Python and/or R. Experience with the formulation and implementation of mathematical optimization problems can give you a competitive edge. The candidate should
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. radiomics) in image processing and in programming (Python, C, Matlab etc.) Fluent in English, German is beneficial Our offer We offer an international, highly stimulating research environment with excellent
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science, chemistry, electrical engineering or a related discipline Strong background in materials informatics and data science Proficiency in Python programming Experience in machine learning for materials