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the state of the art in the fields of smart buildings, digital twins, and the development of building energy optimisation algorithms; Use of methods and tools for energy systems modelling and optimization.; 3
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of SCADA data, Artificial Intelligence algorithms, and the Digital Twin of photovoltaic assets, allowing the identification of anomalies and patterns indicative of imminent failures.; ; The main objectives
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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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and caching algorithms.; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Responsibilities under the grant:; - Design of a key-value storage system that takes advantage of different remote and
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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