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Statistics section, include: Algorithms , focusing on online and approximation algorithms, graph and parameterized algorithms, string algorithms, data structures, combinatorial optimization, algorithmic
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), including algorithm design, implementation, and experimental validation conduct research on distributed processing schemes for hearing aid algorithms, integrating advanced signal processing methods, low
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PhD level. Work with experts at the Jülich Supercomputing Centre (JSC) to run your algorithms/tools on large distributed computer systems. Write reports and research articles, as well as grant proposals
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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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distribution p(A) is typically incorporated in a Bayesian framework (e.g. enforcing that neighboring pixels are highly correlated). An additional difficulty here is that A is a structured geometric object: an
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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