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sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal experiments group (You
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of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in psychiatric disorders
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analytical models Physics-informed machine learning for deformation modeling and prediction Integration of perception, planning, and control for robust real-time robotic performance Requirements Ph.D. in
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the direction of the Principal Investigator in building a first-of-its-kind Software as a Medical Device (SaMD) that predicts, detects, and manages SSIs by fusing RGB + thermal wound images
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission
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human health trajectories through advanced digital twin frameworks. This position offers an extraordinary opportunity in the field of computational health modeling that integrates clinical, genotypic
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years from January 2026, with possibility for extension. The position is associated to the ERC Synergy project RECLESS (Recycling versus loss in the marine nitrogen cycle: controls, feedbacks, and the
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interaction will be modeled as a homogenized continuous medium within the framework of poro-mechanics, while the chemo-mechanical coupling will be generated by chemical potential equilibrium through osmosis