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, networking, and international collaboration Supportive environment fostering creativity, innovation, and interdisciplinary exchange Apply now via our career portal until 29.10.2025, including Cover letter
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to excellent scientific and technical facilities for your work NETWORKING & EXCHANGE: You will participate in (international) conferences and project meetings and actively build your scientific network
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environment with strong immunology focus. To become part of the Graduate school with additional workshops, lectures, networking events and conferences (www.bigs-immuno.uni-bonn.de) Participation in national and
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applications as PhD Student Position (f/m/d) (Ref. 25/11) in the Leibniz Junior Research Group “Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible
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the development of scientific networks in an international environment of the Göttingen campus and beyond. GAUSS provides the organisational and regulatory umbrella for this programme. It was founded in 2005 by
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Karlsruhe Institute of Technology (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 1 month ago
blockchain network based on this protocol and analyze its security Scientific communication. You publish your results and present them at conferences Teaching. You contribute to teaching activities
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have access to excellent scientific and technical facilities for your work. Networking and exchange: You will participate in (international) conferences and project meetings and actively build your
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academic, clinical, and industry partnerships, as well as global networks, we translate biological insights into innovations for early detection, individualized therapies, and disease prevention. Founded in
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part of the DFG-funded SFB 1361 “Regulation od DNA Repair and Genome Stability”. The PhD student will profit from collaboration and networking with other research groups, all focusing on understanding
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did