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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 generated in the scattering
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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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to outreach and dissemination activities, including the drafting and editing of articles and press releases, preparing of dissemination material like brochures and posters, developing material for web pages and
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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 to the studies of protein
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, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's most pressing issues. In research and academic programs
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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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the last 3 years. Applications from all over the world are welcome. Also see the description of Project #1 at https://explora-network.github.io/web/projects.html . Our offer to you • A varied job with
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-network.github.io/web/projects.html . Our offer to you • A varied job with flexible working hours • Free use of local public transport (LandesTicket Hessen) • More than 100 training seminars, workshops and e-learning
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for user-centered collection of digital behavioral data – for example through web tracking, mobile apps, or data donations. Your tasks will be: Contributing to the design and development of data collection
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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