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details can be found at https://www.net-zero-fibe-cdt.eng.cam.ac.uk/ The project is funded in collaboration with Network Rail, the entity responsible for the operation and maintenance of the Great Britain's
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communications into modern large-scale telecommunications networks. We expect a strong background in networking, especially in QKD and 5G environments, particularly in the management and configuration of QKD and
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mechanics) with data-driven techniques. • Proficiency in Python (NumPy, TensorFlow/PyTorch) and MATLAB. • Familiarity with deep learning architectures, particularly recurrent neural networks (e.g., LSTM) and
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information, they are computationally intensive, which prevents their systematic use in large parametric studies. Consequently, simplified or surrogate models (e.g., 1D network models, reduced-order models
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of AV/IT systems to include computer hardware, digital and analog media presentation systems, audio visual hardware and software, audio engineering and configuration of audio DSP technologies, network
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. This project focuses on fundamental algorithmic questions on geometric networks and, in particular, on geometric intersection graphs: graphs whose nodes correspond to disks or other objects in the plane and that
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be completed during the 2026 and 2027 annual periods as specified in the project proposal. Where to apply Website https://sede.udc.gal/services/electronic_board/EXP2026/004389 Requirements Research
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applications. International cooperation and travel The field of work is highly international. We therefore expect you to actively participate in international networks, conferences and collaborations with
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development interests. Established industry and employer networks. Grant writing skills and administration. Relevant industry experience and certifications. Knowledgeable in microcredential and certificate
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Within the CANGONANO network, Amsterdam UMC is recruiting one doctoral candidate (DC) to carry out research in GlycoNanoMaterials for precision delivery of mRNA/peptide antigens to antigen presenting cells