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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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operation, is not considered in the CMF model due to the challenge to solve the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and
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equivalent in Mathematics, Computer Science, or Statistics Proficiency in one or multiple programming languages (R, Python, Julia, MATLAB) Interest in infectious disease dynamics, public health, global health
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will join a dynamic team and will be directly supervised by Fanny Cazettes (CNRS). He/she will benefit from the expertise of the entire research group and from collaborations with theorists for the more
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in Mathematics, Computer Science, or Statistics Proficiency in one or multiple programming languages (R, Python, Julia, MATLAB) Interest in infectious disease dynamics, public health, global health and
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transporters based on their structural characteristics. Main activities: -Conduct multiple sequence alignment of plant transporters using phylogeny-aware profiles -Computationally predict and then analyze high
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collaboration partners. Recently, the Electrical Energy Systems group received grants for nationally-funded projects in Intelligent Electricity Systems. Therefore, this group currently has multiple vacancies in
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will be part of a dynamic research team applying advanced geophysical methods to study soil–plant interactions. Your main responsibilities will include: Developing and applying high-resolution time-lapse
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fluctuate across tidal to seasonal timescales, as existing data consists primarily of isolated measurements. Understanding coastal Ca²⁺ and Mg²⁺ dynamics across ecological timescales is crucial for advancing