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for computation. This is especially useful for distributed and federated AI, where the input of one node is often a function combining the output of many other nodes. AirComp offers a significant scalability 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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toolchains Familiarity with distributed training/inference, AI system bottlenecks, and performance tuning Prior experience with cloud computing and AI system deployment in production settings
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. The courses you will teach may include bachelor-level courses, such as Computer Networks, Distributed Systems, Computer and Network Security, Operating Systems, and master-level courses, such as Topics in
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distribution, spectrum management and coexistence, tactile Internet, and autonomous transportation. Furthermore, our activities are experimentally driven and supported by the SDR CommLab, the SDN Lab, the 6G
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spatial distributions of active phases across multiple length scales. The PhD student will collaborate with international partners to study the evolution of crystalline and amorphous domains in the hybrid
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the three-dimensional distribution of the phases and their structural parameters. You will optimize the spatial resolution at which the 5D ED data can be obtained. You will also help other researchers
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integrate your tracking code into a code that maps the three-dimensional distribution of the phases and their structural parameters. You will also help other researchers to integrate your code
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-intensive, often being non-interpretable, and being highly brittle towards shifts in data distribution and changes in the problem domain. This fully-funded three-year project, with the possible further
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of distributed MIMO, and/or coordinated multi-AP operation (under study in the Wi-Fi 8 standardisation workgroup), using Hardware Description Language on FPGA, based on the open-source openwifi project (https