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cycle or non-award courses of Higher Education Institutions. Preference factors: • Prior work with radar, video processing, or sensor fusion technologies.; • Familiarity with data acquisition frameworks
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of algorithms for analyzing physiological and biomechanical signals acquired by devices with integrated sensors, which will perform electrocardiography, electromyography and movement measurements, among others
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sensors, which will perform measurements, for example, of electromyography and inertial data Objective and efficient feature identification and recognition of personalized patterns for integration
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of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
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of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Applying anomaly detection algorithms for streaming network data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND
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propose a new design and implement a proof of concept for a tiering-based storage system that leverages multiple hierarchical storage devices, such as persistent memory, NVMe SSDs, and CXL, and that enables
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integration and thermal networks; • proficiency in Python; Minimum requirements: - knowledge in programming; - fluency in English and Portuguese (written and spoken). 5. EVALUATION OF APPLICATIONS AND
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neural networks, enabling us to estimate the reliability of a single decision of this algorithm. Regarding generalisation, recent self-supervised learning paradigms have strong synergies with the multi
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. Functional testing of developed PCBs, including experimental validation of electronic circuits and S-parameter characterization using a vector network analyzer (VNA).; 4. Experimental characterization