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; • Organization, systematization, and management of laboratory data. The candidate will also participate in the integration of experimental results with bioinformatics analyses and machine learning methodologies
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Santos do Carmo Madeira IV - Work Plan / Goals to be achieved: Federated learning (FL) has emerged as a promising method to address reliability and safety problem by enabling decentralized model training
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assessed according to the following weights and criteria: - Criterion 1: Absolute merit of curriculum vitae - Criterion 2: Academic performance in the areas of Machine Learning, Data and Information Fusion
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' performance will be assessed according to the following weights and criteria: - Criterion 1 - Knowledge in the areas of Bioinformatics, Artificial Intelligence and Machine Learning - Criterion 2 – Motivation
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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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final jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.IX - Jury Composition: President: Sandra Isabel dos Santos Anjo Effective Members: Luís Filipe
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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