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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: 1. Define a generalist and parameterizable architecture with common capability profiles and interfaces for various types of systems (AUV
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knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous knowledge of physiological signal processing. 5
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parallel programming and/or high-performance computing, particularly on GPU or FPGA architectures; Knowledge of compression techniques, including predictive coding, filter banks, transforms, and statistical
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the following main activities:; 1. Design and implementation of the SQL-to-eBPF compiler: definition of the architecture that translates SQL queries into eBPF programs, and implementation of a prototype capable
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governance definitions of IDSA, the Gaia-X standards, relevant European directives, and best practices defined by reference projects. The issues to address involve the architectural and technical definition
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filters, nonlinear filters, and smoothing methods; Characterization of communication, storage, and processing requirements associated with different monitoring architectures; Adaptation of processing
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baselines (2) Define a multi-agent architecture based on LLMs (3) Implement the multi-agent architecture according to an established framework (4) Integrate the architecture with pre-trained models. 3. BRIEF