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twins, enabling real-time stress-testing under simulated edge cases like cyber-physical attacks and sensor failures. The Research Challenges There exists a complex interplay of factors that present
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Project advert Modern artificial intelligence (AI) increasingly relies on combining multiple sources of information, such as sound, motion, images, and sensor data, to achieve robust and intelligent
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and to better understand the physiological mechanisms of resistance to abiotic constraints. The acoustic signature, integrated into the algorithm controlling the autonomous acoustic sensors, will
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network Research Fields: Hormones, Digital Health, Medical Sensors, Physiology Secondments: University of Ulm (Germany): Algorithms for wearable data analysis University of Manchester (UK): Mathematical
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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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– integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis and treatment of adrenal diseases. Digital medicine is entering a new era where human “digital twins” and sensor-based
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training courses and workshops of the ENDOTRAIN network Research Fields: Hormones, Digital Health, Medical Sensors, Physiology Secondments: University of Ulm (Germany): Algorithms for wearable data analysis
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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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and spatially complex nature of MRI signals. Each MRI examination involves multiple pulse sequences, with signal acquisition being sensitive to coil placement, sensor geometry, B0/B1 inhomogeneities