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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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, in Africa and Europe. Requirements include a university degree (master's or equivalent) in archaeometry, mineralogy, or materials science, as well as the motivation to develop fundamental new data
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to work on fundamental new data relevant to archaeological research and a willingness to work in a team. A further requirement is a willingness to undertake several weeks of fieldwork (collection of raw
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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research. As an interdisciplinary team, we work in a well-equipped building located directly adjacent to Tübingen’s historic old town. More information about us and our institute can be found at www.hib.uni
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scientific hypotheses and questions Conducting scientific experiments and analysing data Writing peer reviewed scientific publications Actively interacting and collaborating with other partners in
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, testing the results against experimental data and deriving interpretative and predictive scenarios. An active contribution to the software algorithms is also wished. Teaching duties come with this position
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