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(SGB) for genomic prediction. The goal is to improve SGB’s performance under data contamination, building on the robust random forest developments from Task 2. This includes investigating robust
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of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
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17172 (COMPETE2030-FEDER-00864900) co-funded by the ERDF - European Regional Development Fund through Innovation and Digital Transition Program - COMPETE 2030 under the scope of Portugal 2030 and by
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; - collaborate in the preparation of technical reports on the algorithms, mechanisms, models, or protocols developed; - develop new modules to enable the simulation and/or experimentation of emerging wireless
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of a multi-modal dataset.; - Implementation of a software module for storing datasets according to a pre-defined standard.; - Development of routines for testing existing ML algorithms on a multimodal
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optimizations, such as new data caching algorithms. Develop a prototype that integrates the optimizations and experimentally evaluate the prototype.; Integrate the optimizations with a new modular and flexible I
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programme of R&D projects geared towards the development and implementation of advanced cybersecurity, artificial intelligence and data science systems in public administration, as well as a scientific
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waveguide setups and development of PCBs for reconfigurable intelligent surfaces (RIS).; 2. Implementation, testing, and optimization of RIS control algorithms on microcontroller-based platforms.; 3
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of Article 4 of Decree-Law No. 74/2006, in its current wording, provided that, under subparagraph e) of Article 3 of RBI-FCT, they are developed in association or cooperation between the higher education
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referred to in subparagraph e) of paragraph 3 of Article 4 of Decree-Law No. 74/2006, in its current wording, provided that, under subparagraph e) of Article 3 of RBI-FCT, they are developed in association