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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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-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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of integrated and inertial navigation. The working group deals with multisensor systems, i.e. the sensor fusion of GNSS (GPS, Galileo, GLONASS, Beidou), INS (inertial navigation system), cameras, LiDAR (light
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ABOUT GHENT UNIVERSITY Phd position Markerless Motion Capture using Wearable Sensors Ghent University is a world of its own. Employing more than 15.000 people, it is actively involved in education
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) and is for 24 months. The researcher will develop 3D mapping and reconstruction algorithms with relevance to mobile robotics
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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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into embedded prototypes to demonstrate real-world feasibility. The overarching goal is to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems
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University's Department of Computer Science. Supported by significant funding, Profs. Himanshu Gupta and CR Ramakrishnan conduct research in the general area of quantum networks, quantum sensor networks, and
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: Haukeland University Hospital (HUH) and University of Bergen (UiB), Norway Supervisors: Prof. Marianne Øksnes , Prof. Walter Karlen Duration: 3 years (with possibility of extension) The project includes: Case