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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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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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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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high throughput PDU equipped with VOC sensors, an algorithm, and a database. The PDU will enable rapid (Ultimately, the PDU is intended to be a cost-effective, user-friendly, adaptable, and efficient
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Dark Matter Search with ADMX and the Quantum Sensors for the Hidden Sector Collaboration School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof E Daw Application Deadline
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(Commonwealth Scientific and Industrial Research Organisation, Australian Government - CSIRO) in Australia will develop the world’s first open-access AI platform to address these challenges by streamlining
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, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault diagnosis, and early fault prediction in electric vessels
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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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Mixed Field Neutron and Gamma Ray Sensors for Precision Farming School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Patrick Stowell, Dr Rhiannon Jones Application
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Distributed radar systems comprise a coherent network of spatially distributed sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation