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extended from cloud solutions (such as OpenLLMetry), the research question is how to identify anomalies in collected information that can come from multiple AI services either invoked manually by users or by
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to evaluate. The main research question is how to automatically harmonize the retrieved information allowing a unique analysis and to map them against multiple user-tailored outputs. This is necessary as the
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environment for PhD candidates, with multiple seminars, working groups, colloquia, and a doctoral school, which also gives access to multiple training opportunities, including courses on general research skills
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use by the group employ multiple-input multiple-output (MIMO) technology and can be connected to build a distributed and cooperative network. To develop signal processing techniques, the group
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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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the implementation of the systems on full-scale bridges. Multiple full-scale case studies of bridges in Europe (Switzerland, Luxembourg, …) will be used to validate the developed solutions and compare