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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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. The project involves the deployment of optical fibre sensor technologies for real-time evaluation of bridge integrity and performance. You will work closely with academic and industry partners to implement
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Role Description A research position is available to support research to enhance observations of human exposure to air pollution and other pollutants using sensor-based approaches. Air pollution is
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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under the supervision of Prof. Rachel Tribe and Dr Martinez-Nunez. The postholder will also benefit from the vibrant and growing bioinformatics community led by Dr. Alessandra Vigilante (lead of the
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for hardware interfacing, sensor integration, and communication protocols (e.g., CAN, I2C, SPI). • Ability to analyse and interpret experimental data using statistical and algorithmic methods
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inversion techniques and signal processing. Strong programming skills, Proficiency in scientific computing (e.g. Python, MATLAB, or similar) for algorithm development and data handling. Experience with sensor
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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key role in developing and producing advanced optoelectronic devices for applications such as quantum technologies, integrated optical sensors, and laser optics. In this EPSRC-funded project, you will