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Field
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courses in HDS domains to learn advanced health data sciences methods that they will later apply in a practical research setting for their individual research projects. Research: Interactive seminars and
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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of research results or willingness to acquire such experience willingness to conduct a research stay for several month at another institute very good knowledge of MS Office, including Word, Excel and PowerPoint
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work together, in order to learn from one another and generate new knowledge and new methods to create a better quality of life in our world. DWI offers you a wide range of possibilities to develop
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work together, in order to learn from one another and generate new knowledge and new methods to create a better quality of life in our world. DWI offers you a wide range of possibilities to develop
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networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In this project
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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-renowned scientists in a competitive yet collaborative environment. This is an environment rich in interaction with other students, postdocs, and scientists. Numerous research topics spanning all
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specialists in an experimental or a theoretical discipline but also understand enough of the other disciplines to speak a common language and engage in fruitful exchange and collaboration. The IMPRS for Genome
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) - a collaborative project run by the Departments of Biology/Chemistry, Physics and Mathematics/Computer Science/Applied Systems Science. The goal is to increase the multidisciplinary aspect of students