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of Évora, with very relevant experience in a large model for evaluating orthopedic implants. The candidate will contribute to (i) the bioreactor production of extracellular vesicles, (2) the isolation and
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for students with BSc degree with reference number BI|2026/882 under the scope of the Projet SALVE: Securing Artificial Language Models Against Vulnerability Encoding (2024.14936.PEX), funded by Fundação para a
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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simulations; (ii) diagnose and attribute the physical mechanisms driving inter-model differences in recovery; and (iii) design and run targeted mechanism-denial experiments (e.g., in CESM and related Earth
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during wind turbine maintenance by experimentally modelling these factors. Through controlled experiments using Virtual Reality (VR) simulations, combined with physiological sensors (such as fNIRS, IMUs
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lasers to atomic transitions, fiber optic design and operation, multilevel rate equation simulations, and careful characterization of systematic effects. Key responsibilities will include but are not
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degradation (LDPM – Lattice Discrete Particle Model) informed by microstructural chemo-mechanical simulations (Kinetic Monte Carlo); for O3, accelerated degradation tests of degradation processes, such as DEF
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-high-resolution models to test whether the discrepancy is primarily a consequence of insufficient realism in earlier modelling. A central part of the project is quantitative validation of simulated
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stellar tidal streams) around disk galaxies. The successful candidate will design, perform and analyse idealized and cosmological hydrodynamical galaxy formation simulations, develop or improve models