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manipulators capable of adjusting their trajectory and resistance in real time in response to variable external loads. This module should integrate learning algorithms based on artificial intelligence, allowing
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: Bachelor's degree program in Electrical and Computer Engineering, Computer Engineering, Artificial Intelligence The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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Online Outlier Detection Project: Online Artificial Intelligence for IT Operations 2.0 Scientific Advisor: João Mendes Moreira Duration of the contract: from 2026-01-01 to eventually renewable. Location
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cycle or non-award courses of Higher Education Institutions. Preference factors: - Excellent performance in programming, software development, and artificial intelligence courses.; - Advanced knowledge
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requirements: Student currently enrolled in a Bachelor's degree program in Electrical and Computer Engineering, Computer Engineering, Artificial Intelligence, or a graduate in the same fields. The awarding
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, Artificial Intelligence or related field.; Currently enrolled in a PhD programme in Computer Science or similar field.; The awarding of the fellowship is dependent on the applicants' enrolment in study cycle
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on the field. Objectives: The Centre for Biomedical Engineering Research (C-BER) develops web and mobile applications with the aim of taking the artificial intelligence algorithms that C-BER has been developing
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(Ionic); - experience using Figma; - experience with the languages C, Python, Java, JavaScript, TypeScript, Haskell, Kotlin; - experience with Docker; - experience implementing artificial intelligence
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production systems, industrial manipulators, and artificial vision.; - Expand knowledge of the state of the art in the specific scientific area of the scholarship; - Identify and select appropriate methods