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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Applying anomaly detection algorithms for streaming network data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND
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testing. The work is expected to strengthen the reliability and predictive accuracy of SGB and related machine learning methods, i.e., those that share common algorithmic steps, in contaminated settings. 4
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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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; - 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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AND TRAINING: - 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
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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 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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sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED models. The development of these compact models will involve collaboration with several
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application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial peptides Perform and support experimental studies across the METAMIC project
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and innovation catalyst, in this exciting project, you will develop novel algorithms to monitor and analyse workers' movements, detect harmful movement patterns, and implement simple intervention