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Field
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vehicle. Multiscale simulations of the downstream expansion behaviour of the residuals for different atmospheric layers. Definition of suitable interfaces to process data for the climate models. Expected
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15.09.2025, Wissenschaftliches Personal The Chair of Marketing Analytics, which is part of the Heilbronn Data Science Center and the TUM School of Management at the TUM Campus Heilbronn, is seeking
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 27 days ago
10 Nov 2025 Job Information Organisation/Company Leibniz-Institute for Food Systems Biology at the Technical University of Munich Research Field Biological sciences Chemistry » Computational
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University of Munich Qualifications The applicants should possess: An excellent or very-good university degree in Economics Strong analytical and methodological skills with a focus on quantitative data
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mathematics Proficient in programming (Python and C++) Fluent in written and spoken English; knowledge of German is an asset Strong analytical and independent working style Excellent teamwork and communication
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in high-performance computing, numerical simulation Fluent in written and spoken English Independent, analytical, and team-oriented working style You are independent, motivated, and passionate about
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Strong analytical and independent working style Excellent teamwork and communication skills You are independent, motivated, and passionate about shaping the energy transition—if this describes you, we
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of the re-entering vehicle. Multiscale simulations of the downstream expansion behaviour of the residuals for different atmospheric layers. Definition of suitable interfaces to process data for the climate
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micropollutants in the catchment area, the design, implementation and evaluation of monitoring campaigns, and the planning and analytical support of laboratory experiments on the degradation and mobility behavior
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty