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Field
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to seamlessly integrate complex hormonal data, high-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms
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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
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to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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decision-making. Examples include crowd management and large-scale communication networks based on cellular or wireless sensors. For instance, during mass gatherings such as the sport matches (e.g
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impacts and suboptimal decision-making. Examples include crowd management and large-scale communication networks based on cellular or wireless sensors. For instance, during mass gatherings such as the sport
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available in Digital Endocrinology in the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient