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field * Strong background in mathematical and computational sciences * Experience with large-scale machine learning, foundation models, or data-centric AI is a plus * Driven, with a strong work ethic and
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stimulating research environment composed of an ambitious and excellent faculty and a large network of research institutions and facilities. The MGSE does not charge tuition fees for attending the doctoral
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Pathogens, a WHO Collaborating Centre, and a member of the Leibniz Research Association. The Computational Infection BiologyDepartment, led by Thomas Otto, is seeking a highly motivated PhD Student (in data
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project "Information Field Theory for Experiments at Large Research Facilities" has a PhD opening in the field of data-intensive radio astronomy. Methods of information field theory will be used
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support and services for the analysis of large-scale omics data. RESPONSIBILITIES: Develop, implement, and maintain bioinformatics pipelines for various omics data, including RNASeq and scSeq besides others
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interdisciplinary journals and conferences, and contributing to open-source research software. The developed methods will be validated using large-scale electron microscopy data from collaborative research projects
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mining. In-depth knowledge of the design, analysis and implementation of algorithms for large text corpora, including efficient data pipelines and clean experimental design. Strong NLP skills for semantic
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, catalysis studies - in particular electrocatalysis and Python programming Experience within the following fields is highly beneficial: Transmission electron microscopy, Electrochemistry, Image analysis, Big
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6 semesters Beginning Only for doctoral programmes: any time Additional information on beginning, duration and mode of study We would like all new doctoral candidates to attend a Welcome Week in
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(4DSTEM). This approach will combine three-dimensional charge distribution data, generated through atomistic simulations, with machine-learning-driven modelling to guide and refine the phase reconstruction