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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
; - Collaborate in the writing of technical reports regarding the protocols, mechanisms, and algorithms developed; - Co-author scientific publications based on the work developed; - Prepare the research grant
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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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10 val. Solid knowledge of machine learning, information visualization, and data analysis algorithms and methods 10 val. Basic knowledge of Extended Reality application development
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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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Transition Thematic Program (COMPETE2030) through the European Regional Development Fund (ERDF) and Portugal 2030, under the following conditions: Work Plan and Objectives to Reach: The work to be developed
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submission from 28-Nov to 16-Dec, 2025 WHO WE ARE INESC-ID (www.inesc-id.pt ) , “Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa” is a Research and Development and
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hours and attendance; Compliance with the work plan, specifically the development of software for the AI module, including the implementation of algorithms, integration with the main application, and
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Regulations of the University of Aveiro. 5. Work Plan: he work plan is integrated into the activities of the NEXUS Agenda, with an emphasis on the development of methods and algorithms with applications in
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Algorithmic Categorization”, 2024.07707.IACDC supported by measure “RE-C05-i08.m04 – “Support the launch of a program of R&D projects aimed at the development and implementation of advanced cybersecurity
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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