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multidisciplinary team to lead a field-based study characterizing volatile organic compound (VOC) exposure across diverse real-world indoor environments. This role offers the opportunity to conduct mobile sensor
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Multi-Sensor Data Fusion and Smart Decision Making School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline: Applications
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programme including 16 PhD students trained in several European countries (https://cordis.europa.eu/project/id/101227338 ). You will be appointed for three years, during which time you will study for a fee
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mobility and vulnerability to falls. The project will combine physiological sensory biomarkers with postural control, gait analysis and real-life mobility measurements obtained using wearable sensors and
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, including cellular and Wi-Fi networks, in order to meet the requirements of emerging critical applications. In this context, intelligent embedded devices (augmented cameras, connected sensors) generate
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: Radio Frequency Identification (RFID) technology, both chip and chipless, along with the integration of stand-alone or integrated sensors, provides a physical platform for the implementation
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to apply these modelling techniques to support the development of novel experimental technologies and AI-assisted optical sensors. Supported by the Structured Photonics Research Group at the University
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optics, environmental sensors, and next-generation biomedical technology development. As the project focusses on training, there is an expectation that the candidate will submit a PhD in the field relevant
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microorganisms and their cell-free transcription/translation systems Learning plasmid design and purification Developing skills in the optimization of sensor components and sensor function Participating in multi
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years, including the C-band European Space Agency (ESA) ERS-1/2 and ASCAT sensors beginning in 1991 to current, the Ku-band NASA‘s NSCAT QuikSCAT, SeaWinds, and ISS-RapidScat scatterometers, and the Ku