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reproductive isolation. This will largely advance our still limited knowledge on the intricate mechanisms of CHC perception in particular and on speciation mechanisms mediated by chemosensory evolution in
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Chair of Biological Imaging 11.07.2023, Wissenschaftliches Personal We now seek a highly qualified and motivated PhD student (f/m/d) to design, develop, and test novel optoacoustic sensing platforms
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competitive, scientific environment? If you are motivated by the societal impact of science and technology, if you seek an opportunity to play an active role in the development of emerging technologies
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The main task is the further development of methods for the non-destructive investigation of the pyramids of Giza in Egypt (World Cultural Heritage) in cooperation with our partners by using acoustic imaging
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significantly impact next-generation experiments at large facilities. Tasks: This project will develop innovative ultracold neutron detectors, using superconducting microstructures for spin- and energy
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– depending on the successful candidate’s background and interests. Your tasks: Develop new exact and approximation algorithms and perform complexity analyses for optimization problems on (temporal) graphs
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to remote sensing and simu-lation modeling. A particular focus of our work is on mountain forest ecosystems. A quantitative understanding of ecosystem dynamics provides the foundation for the development
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
. Ongoing projects include the development of new techniques for visualizing graphs in Euclidean and non-Euclidean spaces, dimensionality reduction, and user-centered evaluation of visual interfaces
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are pursued by this Center: (1) develop a new set of consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed
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main focus on the development of control software. ▪ You will design and implement advanced control and readout protocols and optimize experimental characterization workflow,s leveraging machine learning