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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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noise and sensor uncertainties. Applied Artificial Intelligence and Data AnalysisProficiency in multivariate analysis techniques (PCA, regression, clustering) and predictive risk models; Experience with
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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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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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question, including possible renewals, an accumulated period of three years in that type of grant, consecutive or interpolated. 5. Work plan: Use of artificial intelligence tools, computational modeling, and
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: Knowledge of Applied Security; Knowledge of Artificial Intelligence; Knowledge of Privacy and Data Security; Knowledge of Intrusion Tolerance; Knowledge of Cybercrime and Forensic Analysis; Knowledge
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and Artificial Intelligence, as well as knowledge of programming languages and libraries such as Python, OpenCV, PyTorch, TersorFlow or similar. 3. Project Objectives: The work to be carried out
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. Preferred factors: Solid knowledge in embedded systems and Artificial intelligence technologies. Workplan and objectives to be achieved: The work plan is part of Work Packages WP1 and WP2, to be added in any
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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