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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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Applications are invited for a Doctoral Candidate in SHIELD (Strategies for Healing Implant-Associated Infections and Enhancing Longevity in Devices) Doctoral Network. The candidate would be working
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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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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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PhD: Recognizing Multicultural Strengths of Youth via Social Networks at Work Faculty: Faculty of Social and Behavioural Sciences Department: Social Sciences Hours per week: 36 to 40 Application
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The SigCom research group of SnT, headed by Prof. Symeon Chatzinotas, focuses on wireless/satellite communications and networking. The research areas focus on the formulation, modeling, design, and
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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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NPL, the UK’s national metrology institute, is developing a fully functioning, thoroughly characterised, local area quantum network (QLAN) linking heterogeneous nodes across its site. It will make
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-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate