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monitoring frameworks, and on measuring the energy impact incurred over different computational resources. Minimum requirements: - Experience with software-defined control systems (e.g., Cheferd, PAIO, PADLL
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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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of a multi-modal dataset.; - Implementation of a software module for storing datasets according to a pre-defined standard.; - Development of routines for testing existing ML algorithms on a multimodal
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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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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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PRESENTATION OF THE WORK PROGRAMME AND TRAINING: • Collaboration in the design and prototyping of a radar + video system for motorcycles.; • Development of software modules for automated data collection
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of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - Experience with software installation and configuration
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The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - Experience in software testing implementation
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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