626 network-coding-"Chung-Ang-University"-"Chung-Ang-University" positions at Nature Careers
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the Guilliams and Saelens teams. Tissues, both in homeostasis and in disease, are built as networks of interacting cells that maintain or manipulate each other’s identity. In the liver, we have identified a 4
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One position as Neuroinformatics communication and networking advisor is available at the Institute of Basic Medical Sciences, Department of Molecular Medicine, for a period of 2 years, with
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-funded research institute at the Cardiovascular Center is a member of the DFG Excellence Cluster CPI, the German Center for Cardiovascular Research as well as the CRC TRR267 “Non-coding RNAs in
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candidate to undertake research in the area of cognitive networking for 5G/6G networks, with a focus area on connected and autonomous mobility. This research will explore how artificial intelligence can be
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The Dynamics of Biomolecular Networks (DYNABIO ) cluster of excellence at the Université Côte d’Azur (Nice, France) brings together 85 research teams from six local biology institutes: C3M (Centre
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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this Ph.D. topic proposal: • The optimal approximation of 3D shapes using meshes is known to be a NP-hard problem. This means that finding the best possible mesh representation for a given shape, while
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Position Reference code: 50136009_4 ? 2025/KD 2 Commencement date: as soon as possible Work location: Geesthacht (near Hamburg) Application deadline: September 21st, 2025 We are looking for a highly
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of several researchers working in the field of inverse problems due to their ability of combining variational inference approaches with the ability of neural networks to learn unknown posterior distributions