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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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for diversity Desirable knowledge and skills include: o Understanding of the principles that define DNA, RNA, protein structures, functions, dynamics and interactions o Experience in plant work
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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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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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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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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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international collaborators’ labs. The applicant should: • have interest in developing and/or applying novel chemical probes to investigate biological functions, • have sound knowledge of organic chemistry, both
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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