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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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biological techniques, as well as animal models, will be applied. We offer: Embedding in an internationally competitive scientific environment An established, broad spectrum of scientific methods A wide range
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Description We invite applications for a fully funded PhD position within the DFG Priority Programme SPP2389. https://tu-dresden.de/mn/biologie/allgemeine_mikrobiologie/spp2389?set_language=en
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English We offer: a stimulating, world leading research environment on biomolecular condensates embedded in a focused, interdisciplinary structured training program and close mentoring access to advanced
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the systematic exploration of polynomial-time tractability in the field of constraint satisfaction and its extensions, in particular promise CSPs, valued CSPs, and CSPs over infinite domains. The work is embedded
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gas/solid interfaces. The project is embedded into interdisciplinary and international cooperations involving partner groups from natural science and engineering ( https://www.sfb1452.research.fau.eu
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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the development of digital twin concepts and their application to promote regional material self-sufficiency in the construction industry in line with net zero targets. It is embedded in a Joint Research Lab
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the development of digital twin concepts and their application to promote regional material self-sufficiency in the construction industry in line with net zero targets. It is embedded in a Joint Research Lab
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into consideration. Please submit your complete application by sending it to secai-office@tu-dresden.de , preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de . Application Deadline