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Field
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. To minimise material and energy wastage, digital models of the manufacturing processes can be developed and linked to process control and optimisation. State-of-the-art digital models and AI tools
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the service life of infrastructure. The research content includes: real-time data analysis and modeling pipeline network dynamic prediction and state identification optimisation algorithm and high-performance
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Experience with life-history, developmental biology, or behavioural ecology experiments in small invertebrates Experience linking empirical data to modelling or theory Experience with controlled laboratory
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signatures in communication and coordination data predict collective outcomes — such as group spacing changes, behavioural synchrony, and leadership transitions — beyond what pairwise models capture
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mangament in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. Scientific context Many engineering and
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and time-series modelling techniques will be used to predict delay and performance degradation. The predictive models will be integrated into a real-time robotic system and evaluated in realistic
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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applications. Grid-interactive efficient buildings rely on emerging digital and cyber-physical systems to optimize HVAC operations for both energy efficiency and demand response. While Model Predictive Control
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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-enabled infrastructures. ToM-Enabled Multi-Agent Cooperation: Equip autonomous agents to model the mental states of other agents, allowing prediction of behaviours, conflict resolution, and coordinated