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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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mobile applications in the health field (e-health & m-health). Development and integration of algorithms from the research group. Testing and validating technologies in real scenarios, collecting user
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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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visualisation (libraries such as Three.js, OpenGL, VTK, or similar); - Advanced knowledge of optimisation algorithms; - Previous experience with software development for logistics problems; - In-depth experience
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algorithms to combine information on cardiovascular activity obtained from heart sound signals, electrocardiogram, and photoplethysmography. Investigate the inclusion of prior knowledge about the application
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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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process and the results obtained. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - To contribute to the specification and development of algorithms for optimizing energy systems with
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: • Experience in developing image processing algorithms for weeding or pollination; and; • Experience in developing micro drones with potential for weeding/pollination. Funding Entity: on the scope AGROBOOST
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manipulators capable of adjusting their trajectory and resistance in real time in response to variable external loads. This module should integrate learning algorithms based on artificial intelligence, allowing
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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