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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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/ PINN models on actual equipment, and evaluate their performance against both simulations and field measurements; - Extend OCTO Technology’s open source software (https://github.com/octo-technology/VIO
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processes studied, specifically high moisture extrusion, drying, and ripening processes, useful to understand and control the mechanisms of texture development. - To study and model the relationship between
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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to recognize and critically reflect on the influence of both linguistic and multimodal forms of communication. For example, how words like “riot” versus “demonstration” frame the same event very differently
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empirical and remotely sensed data Compilation of data for running a simulation model, model evaluation against independent data sources Assessment of future forest development under different management
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analysis, modelling and field experiments, our project will illuminate the way forward for assisted migration (AM) as a pathway to sustain, and to restore in case of degradation, the biodiversity and
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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When robots move, human interaction partners and observers ascribe an intention to the robot. For example, in a simple pick-and-place scenario where a robot is facing two different objects, as its
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data to decode multisensory information Investigate how neural representations change across different brain states (awake, asleep, engaged) and track representational drift over extended time periods