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Master’s degree (or equivalent) in mathematics, computer science, physics, or related field. Sound knowledge in (scientific) machine learning, and knowledge in numerical analysis and numerical linear algebra
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to impart both scientific and methodological knowledge and offers the opportunity to regularly present doctoral projects in internal events and benefit from scientific exchange. You can find information
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to impart both scientific and methodological knowledge and offers the opportunity to regularly present doctoral projects in internal events and benefit from scientific exchange. You can find information
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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out whether the resulting products could influence the aroma of processed Brassica vegetables. Tasks include planning and conducting plant cultivation and food chemistry experiments chemical analysis
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analysis of mobility behavior, the application of agent-based models and road space analysis, particularly for road safety. Research on walking and cycling as a cross-cutting issue has a high priority in all
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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approaches to efficient analysis of the vast data amounts generated in the scattering experiments. For more information, visit our web page: https://www.soft-matter.uni-tuebingen.de/ We are currently looking
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and spectroscopy Construction and optimization of single-molecule microscopy setups Development of image- and signal-processing software for single-molecule microscopy and spectroscopy data Analysis
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PhD). Tasks: The successful candidate is required to conduct research on a topic in probability theory (e. g. stochastic processes / stochastic analysis) and its applications. You support the institute