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for many high-tech companies. Job description This role involves the development of cutting-edge chemical sensors based on metal-organic frameworks (MOFs) that leverage kinetic selectivity to differentiate
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assess risks of de-anonymization, including researching current de-anonymization strategies Deploying and benchmarking anonymization methods. Investigation of state-of-the-art methods from cryptography and
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live in. Your role This position is inside the SPETRA doctoral training unit which investigates new materials, methods and concepts for converting sunlight into usable energy sources. Inorganic
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fields with a socio-technical background Interest in data anonymization and/or technology law will be considered an asset Interest in applying methods commonly used in information systems research (e.g
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Master in Experimental Psychology, HCI, UX or related fields Experience with research methods in social sciences (e.g., empirical research, experiment design) Experience in statistical methods is
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in several of the following key topics: Energy System Technologies Digital Twin Platforms and Data Analytics Predictive Models & Explainable AI (XAI) Methods in Time Series Forecasting Data Retrieval
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Master's degree in Energy Policy, Energy Economics, Information Systems, Mathematics, Operations Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in
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Systems, Economics, Mathematics, Operations Research, Computer Science, or a related field Strong knowledge of quantitative and/or computational research methods, ideally in optimization and simulation
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Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in numerical optimization and simulation models. Proficiency in one of the major programming
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activities: Conduct highly innovative research in the intersection of cybersecurity and safety-critical systems, in dependability methods and solutions and in architectures and systems that support the