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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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sciences (e.g., Soil Sciences, Earth Sciences, Biogeochemistry, Environmental Sciences, Ecology, Biology) or related subjects and strong knowledge in the dynamics of organic matter in natural systems
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diagnosis, and therapy of diseases like cardiovascular diseases or cancer. Overall, the institute strives to advance precision medicine by combining knowledge from different fields such as biology, chemistry
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HPC environments Good communication skills to interact with collaborators ranging from machine learning researchers to pathologists or medical students Knowledge of biology and medicine is a plus Highly
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, Radioecology, Mineralogy, Geology or related field Experience in experimental and analytical work and in the evaluation of extensive analytical data sets Knowledge of handling radioactive substances is desirable
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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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) and should be used for a doctorate. Participation in the accompanying doctoral program is compulsory. This serves to impart both scientific and methodological knowledge and offers the opportunity
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(social sciences, political science, economics etc.) Desirable: - good knowledge of quantitative tools of empirical social science research. - experience in academic work; - knolwegde of university
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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Completed university studies (Master/Diploma) in the field of Chemistry, Radiochemistry, Radiopharmacy, Radioecology or related field Excellent knowledge of a broad range of organic chemistry and analytical