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advanced seismic methods (including array processing, machine learning, and potentially distributed acoustic sensing) to develop novel approaches for monitoring unsteady and non-uniform flood flows across
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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industries: in-car systems, medical devices, phones, sensor networks, condition monitoring systems, high-performance compute, and high-frequency trading. This CDT develops researchers with expertise across
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modelling. Some experience with programming in R and/or Python. Exposure to climate or weather data, forecasting systems, or geospatial tools. Understanding of or curiosity about machine learning, AI
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monitoring. To address these limitations, the proposed research will integrate UAV-based imaging, satellite remote sensing, and AI-supported classification workflows to quantify lichen distribution at multiple
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skills/interests: Wireless networking, signal/array processing, machine learning, optimization, hands-on experience with hardware and systems. Objectives: To infer the status of distributed, heterogeneous
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monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
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Researcher (R2) Positions Master Positions Country Ireland Application Deadline 1 Dec 2025 - 23:45 (Europe/Dublin) Type of Contract Temporary Job Status Full-time Hours Per Week 39 Is the job funded through
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driving, in-car monitoring, industrial automation, and security surveillance. The research, called "R4DAR," aims to leverage emerging 4D imaging technology with Massive MIMO to create image-like radar
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data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research findings