22 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships at Nature Careers in Germany
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the network training as secondments and events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen
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events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion
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Research Program RTG3120 on Biomolecular Condensates (https://dresdencondensates.org ). Each PhD project is part of an interdisciplinary framework that includes shared training activities, and supervision by
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elucidation and (bio-)chemical methods, interconnecting biochemistry, pharmacology, and medicine and foster the individual development of young researchers. Further information: https://www.medizin.uni
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. Information about the available topics and supervisors can be found under the following link: http://scads.ai/positions2025. The location of work (Dresden or Leipzig) depends on the topic assigned for each
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the application of female scientists. Additional information: Please submit your application until 7th January 2026, indicating the reference number 27635, via our online portal: Apply now! https://jobs.uksh.de/job
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nationalities. Your application Find details about the IMPRS, our research, and our online application procedure at: https://www.mpimp-golm.mpg.de/IMPRS-PhD Closing date for applications: 10 January 2026, 23:59
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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PhD student (m/f/d) in the field of chemistry, chemical engineering, materials science or comparable
polyoxometalates Using suitable characterization methods to characterize the synthesized materials Using machine learning tools to tune the synthesis parameters in a feedback loop and enhance the properties
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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor