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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms to automatically identify, flag, and mitigate data artifacts
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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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sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments
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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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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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training network including workshops, courses in topics relevant for the project. Research Fields Endocrinology, Digital Health, Medical Sensors, Systems Physiology, Internal Medicine Secondments
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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between
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fundamentals of networking. Objectives: To achieve on-device spectrum sensing using on-board sensors of mobile BSs, empowered by embedded deep learning algorithms; to propose an analytical model for the cell
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Health, Medical Sensors, Systems Physiology, Internal Medicine Secondments : University of Ulm (D): To work with algorithms for wearable data University of Manchester (UK): To learn mathematical modelling