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machine‑learning or data‑analytics tools High‑level programming skills (Python, R, Julia) to build, test, and optimize models of geochemical systems Interest in large‑scale computational simulations (e.g
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well as surface and bond properties Determination of sorption properties of An-MOFs towards fission products Development of novel An-MOFs with optimized sorption properties Presentation of the results in scientific
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the Leibniz Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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the English Our offer A vibrant research community in an open, diverse and international work environment Scientific excellence and extensive professional networking opportunities A structured PhD program with
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offers access to state-of-the-art infrastructure and a vibrant scientific community. Join us in developing solutions for a rapidly changing world and help shape the future by working in an international
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optimization and sensitivity experiments for each portfolio design problem to explore the outcomes for correlation and causation between the metrics, groups, and network changes, and establish Pareto optimal
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offers access to state-of-the-art infrastructure and a vibrant scientific community. Join us in developing solutions for a rapidly changing world and help shape the future by working in an international
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uptake, a relationship that dates back 450 million years and remains vital for major crops. Yet, these symbioses are not fully optimized for today’s intensive agriculture. This project aims to uncover