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distributed systems. Minimum requirements: - Solid knowledge of database engine implementation; - Solid knowledge of optimization algorithms (Volcano/Cascades); - Practical development experience with
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limited to, deep reinforcement learning, federated learning, deep learning and meta learning. The overall aim is to improve the efficiency in cyber-physical systems, such as drone swarms, that support 5G
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; (d) Participation in testing and validation activities in an operational environment (testbed in a real water distribution network); (d) Production of technical documentation and contribution
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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
; - survey and analyze the state of the art in emerging wireless networks, including simulation aspects; - collaborate in the preparation of technical reports on the algorithms, mechanisms, models
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of algorithms, data models, and interfaces. Research and selection of the tools and technologies to be used. • Initial development and prototyping: Start of development of the core components of the malicious
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. • Design and specification: Definition of the detailed architecture of the modules associated with the detection of malicious activities. Specification of algorithms, data models, and interfaces. Research
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algorithms. The aim is to develop a high-performance intelligent motor control system that enables greater sustainability, safety and efficiency of the e-bike's energy system, in order to meet the requirements
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the preparation of technical reports on the algorithms, mechanisms, models, or protocols developed; - collaborate in the development of new communications solutions for extreme environments; - contribute to co
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frameworks such as COLMAP and Open3D; Ability to develop algorithms for object detection and tracking, 3D reconstruction, and SLAM. Advanced Control and Intelligent RoboticsSolid knowledge of classical and
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions