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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
resources. To fill this gap, this proposal aims to design novel distributed and lightweight LLMs for spectrum management in aerial 6G networks. Specifically, the project will design wireless-aware data
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intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
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are as important as high accuracy. Supervise student projects at BSc, MSc, and PhD level. Work with experts at the Jülich Supercomputing Centre (JSC) to run your algorithms/tools on large distributed
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Offer Description Mission: Provide support in the longitudinal acquisition of pediatric polysomnographic (PSG) records and contribute to the development and validation of multichannel algorithms
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optimization methods for implementation within the framework of the objectives of the doctoral thesis, starting with the exploration of methods based on genetic algorithms. Explore the possibilities
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algorithms * Parallel algorithms and distributed computing * Parameterized complexity and structural graph theory * Random structures and randomized algorithms * Sublinear and streaming algorithms
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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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Professor will be responsible for teaching and research in some of these areas: computer architecture, media, database, algorithms, parallel and distributed systems, etc. Only shortlisted candidates will be
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sites (k-nearest neighbor algorithm, centroid models, distribution models, etc). We are also expanding on our previous work applying community detection methods, such as modularity maximization and