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Description Reference number 3705 Pay grade 13 TVöD Place of work Berlin Limited for three years Application deadline 10.06.2025 Apply here BfR job [jsp?id=003705en&dbv3_man=allgemein-Datei] portal The BfR independently prepares expert opinions and statements on issues of food, feed and...
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Description Nestled in a modern city surrounded by nature and with an exceptional standard of living, Leibniz University Hannover offers excellent working conditions in a vibrant scientific community. The Department of Phytophotonics at the Institute for Botany / Hannover Centre for Optical...
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Description This PhD project builds on ongoing research projects in the area of photocatalytic membranes at IAMT. Current work will be expanded to (select) novel materials and membrane preparation
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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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scientific challenge. The planned PhD project will therefore investigate the interaction of food-borne toxicants with realistic co-exposure using New Approach Methods (NAMs). The focus will be
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scientific challenge. To address this challenge, the PhD student shall assess the interaction of food-borne toxicants at realistic co-exposure using New Approach Methods (NAMs). The focus of this work will be
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Description Within the Collaborative Research Center “Wave phenomena – analysis and numerics” (CRC 1173) we are currently seeking to recruit, as soon as possible, a Doctoral Researcher (f/m/d – 75 %) in Mathematics for the project “Quantized vortices and nonlinear waves” The CRC has been funded...
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Description This PhD project builds on ongoing research projects in the area of Nanofiltration for Contaminant Removal at IAMT. Novel nanofiltration membrane materials will be examined with
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a dedicated PhD student (f/m/d) for the development of innovative microbial cell factories.
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers