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Field
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to ensure high predictive capability and, on the other hand, keeping the models sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED
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/ is a future-oriented research group with the main strengths and focus topics: Self-driving vehicles, driving algorithms and cyber-physical system Sensor fusion, perception and big data Cybersecurity
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Researcher, of the FCiências.ID Scientific Research Career, within the scope of the project HOFGA: The Hardness of Finding Good Algorithms (Ref. HORIZON-ERC-STG-101041696), financed by the European Union´s
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control algorithms lies a physics-based simulation model, whose accuracy largely determines the effectiveness of the control loop. Position 3 – High-fidelity simulation of the LAFP process Current
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cryptography guarantees better performance and faster speed for encrypting data. Without doubt, AES (Advanced Encryption Standard) and Keccak/SHA-3 (Secure Hash Algorithm 3) are the two most used and famous
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analysis, AI algorithm modeling, testing, and integration into functional systems within the project scope. Specifically, in activities related to behavior modeling from IoT device data, generative AI
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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off-the-shelf sensors and the development of resilient algorithms that combine first-principles modeling with modern machine learning techniques. The goal is to push the boundaries of robust perception
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing