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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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depletion, toxic algae, and pollutants. This natural sensitivity makes them powerful bio-sensors for environmental monitoring, capable of providing early warnings of ecosystem stress. However, harnessing this
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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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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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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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drugs at the target site, using only an internal battery and on-board sensors for fully autonomous operation. The overarching goal is to develop a battery-powered ingestible capsule that autonomously
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parcel delivery and environmental sensing. Equipped with diverse onboard sensors, including cameras and GPS, delivery UAVs hold significant potential for urban sensing applications such as infrastructure
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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
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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