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-of-the-art models for computer vision based on Machine Learning. - Analysis and Study of existing resources; - Analysis of the state of the art in adversarial attacks and adversarial training and their
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-of-the-art models for computer vision based on Machine Learning. Work plan: - Analysis and study of existing resources. - Analysis of the state of the art in universal adversarial attacks on computer vision
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: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 40%; VII.II- I – In the evaluation of the interview, candidates' performance
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%; - Criterion 2: Scientific dissemination actions – 40%; - Criterion 3: Academic performance in courses within the fields of Programming, Artificial Intelligence, Machine Learning, or related areas – 20%; VII.II
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or radiologist) -Analysis of behavioural and fMRI, DTI or anatomical data (depending on what the fellow wants to learn) -Activities, which are required from a PhD student (e.g., pre-defense, attending obligatory
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articles based on the results obtained. The goal of this work is to explore and prototype different integration strategies, evaluate their effectiveness in selected machine learning or neuroevolution tasks
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of an artificial intelligence (AI) solution for the diagnosis of invasive fungal infections, using microscopy images obtained in laboratory settings with limited resources. Leveraging deep learning models such as
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Collaborate in the use of remote sensing and machine learning methods to detect A. longifolia and to monitor the spread and effects of the biological control agent (occasional collaboration). Activity 4