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Project details Objective: This project aims to develop a wireless, nanoengineered graphene-based biosensor for real-time dopamine (DA) detection. The wireless design of the sensor aims to enable
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are urgently needed to monitor PFAS in water and probe their interactions with biological systems. This PhD project will develop a cutting-edge single-molecule optical sensor for real-time, ultra-sensitive PFAS
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to development and climate change. This project will analyse data from innovative motion sensors and a suite of other sensors deployed along the Alaknanda River, a tributary of the Ganges in India, since 2025
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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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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 matches (e.g
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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
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velocity continuity or GNSS-derived velocity estimates. Sensor fusion consistency: aligning pseudorange corrections with complementary sensors, such as inertial navigation systems. Map-based constraints
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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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field studies, and innovative design strategies will be developed that incorporate corrosion-resistant materials, optimised configurations, and embedded Internet of Things (IoT) sensors to monitor
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. The ML will use 500,000 fundus images from open-source and customised retinopathy datasets. We will compare retinopathy grading accuracy by NHS clinician vs ML algorithm. This will build on Exeter’s