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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
transmission mechanisms using Regenerative Artificial Intelligence; - Develop a prototype for image transmission based on regenerative AI semantics; - Integrate and test the solution with an emulated HF testbed
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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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, 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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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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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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(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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optimize reimbursement claims with health insurers. The solution will be based on advanced hyper automation and generative artificial intelligence techniques, focusing on efficiency in terms of performance
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artificial intelligence - Evaluation of interface design, usability assessment, and user experience - Autonomy Minimum requirements: - Minimum score of 70 points in the curriculum assessment. - Availability
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on artificial intelligence (AI) for energy systems and electrical grids - Execution of functional tests to validate the safety and robustness features of AI, taking into account the data-driven nature of AI and
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on artificial intelligence (AI) for energy systems and electrical grids ; - Execution of functional tests to validate the safety and robustness features of AI, taking into account the data-driven nature of AI and