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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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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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) 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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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
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necessary knowledge and a broad range of skills for the successful completion of the project. Basic knowledge of data analysis or programming and good English skills are desirable. We value independent
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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the field of Biotechnology/Biology/Bioprocess engineering or related field Knowledge of genetic engineering, protein production and cell-free approach as well as bioprocess engineering, bioproduction
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support reduction in model uncertainties associated with the estimates of the full N budget at the European continental scale and improve knowledge on the mechanisms governing N translocation publication
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using human primary cells Very good knowledge of written and spoken German and English Desired Practical experience with the application of microphysiological systems Knowledge of bone physiology and