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assessed according to the following weights and criteria: - Criterion 1 – Academic background – 40% - Criterion 2 – Motivation and alignment with the work plan to be developed developed – 60% VII.II
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methodologies for assessing the robustness and safety of fully human-operated, AI-assisted human-operated and autonomous systems, considering risk assessment aligned with the EU AI Act, reliability and robustness
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TRAINING: This work will start with a study on controlled data sharing methods in federated environments. Existing solutions will be tested for creating data spaces that align with the technical and
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the lifecycle of AI systems, from data preparation to model use, monitoring, and updating, should be aligned with human activities, organisational processes, and decision-making in industrial contexts
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cells and the collinear alignment of the pump beam. In parallel, they will participate in the development of a long-range LiDAR test platform, with a focus on experimentally investigating how speckle size
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acquired across multiple anatomical regions to form a patient-level assessment. This PhD project will investigate novel deep learning methodologies that jointly model anatomical structure and prediction