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, Automation, Machine Learning and Artificial Intelligence, Sensor Networks, Hierarchical Decision and Control Systems, with a primary focus on manufacturing and autonomous systems. (1 to 5 points); C
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Artificial Intelligence, Sensor Networks, Hierarchical Decision and Control Systems, with a primary focus on manufacturing and autonomous systems. (1 to 5 points); C. Knowledge and Experience of participating
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first implementation use case, given its interdisciplinary coverage of communication networks, active distribution network management, and behind-the-meter systems. Duration: 3 months Maximum Duration
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field trials using sensors for automated mosquito classification; • Morphological identification and processing of mosquitoes, including pathogen screening within the scope of the REVIVE programme and
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for planning, network modeling, and resource scheduling, considering different usage perspectives and performance criteria; (ii) Contribute to the development and implementation of the different solution modules
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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system in orbit. This system will allow for a connection to be made between a satellite and the terrestrial 5G network, offering IP connectivity to the other subsystems. IST's responsability in the project
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chains and intermodal distribution networks, exploring synchromodal strategies to enhance the efficiency, flexibility, and resilience of the chains.; As part of the fellowship, the selected candidate will
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therein must be completed by the time of contracting. Work plan: (i) Support the design, development, and validation of methodological and computational solutions for planning, network modeling, and
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international networks.; - Ability to prepare reports, presentations and decision-support materials in Portuguese and English.; - Knowledge of health/bio-health, energy, mobility, digital transformation or deep