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backgrounds are ex-pressly encouraged to apply. Female applicants with equal qualifications and suitability will be given preferential consideration. Further information on our equal opportunities measures and
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career level (please seewww.tum.de/en/faculty-recruiting-faq/ for further information). A university degree and an outstanding doctoral degree or equivalent scientific qualification, as
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machine learning, AI, and computational data analysis Solid understanding of biological and biomedical concepts, particularly in genomics, transcriptomics, or single-cell technologies Proven experience
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Demonstrated creativity, independence, and scientific excellence Experience working with large-scale data, modeling, or AI/ML methods is an advantage If you fulfill all the requirements, you may be eligible
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from sending a photo as part of the application. Information on how we protect your data can be found on our website The IfL advocates professional equality for all genders. We value diversity and
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information, please contact Prof. Roland Netz, rnetz@physik.fu-berlin.de Applications should be sent by e-mail, together with significant documents (Cover letter, CV), indicating the reference code, in PDF
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, statistical mechanics, and stochastics • Very good oral and written English skills For further information, please contact Prof. Dr. Roland Netz (rnetz@physik.fu-berlin.de / +49 (0)30 838 55737). Applications
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As the German National Library of Science and Technology our future-oriented services ensure the infrastructural requirements for a high-quality supply of information and literature for research in
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fields. The program has already prepared numerous generations of students for careers in law firms, companies, government agencies, and other organizations. Further information about the program can be
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial