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research environment. Project background Future European power systems are expected to be driven by a very high share of renewable power plants and will be extremely complex, uncertain environments with
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of the heart’s electrical activity, often caused by complex changes in heart tissue. Understanding and treating arrhythmias effectively remains a major challenge. Recent advances in artificial intelligence (AI
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, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where annotations are scarce or unreliable. Recently developed unsupervised learning methods allow
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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Programme in Linguistics or Language Acquisition – a condition. Research focus on L2 or multilingual acquisition of grammar – a condition. Experience in the field is not required. Language skills Knowledge
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Organisation Job description Project and job description Our project will make use sensing technologies (hyperspectral cameras, NIR and Raman sensors), and an edge-compute AI pipeline to sort used
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
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Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics
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, including patch-clamp electrophysiology, high-resolution imaging, chemogenetic manipulation of glial activity, and behavioral assays in rodent models Analyze and interpret complex datasets from