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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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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
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Ph.D. degrees. The Department has research strengths in data mining, databases, graphics, image and video computing, machine learning, natural language processing, networking, distributed systems
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experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will