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Field
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-frequency characterisation. Investigate and minimise measurement artefacts associated with grounding, shielding, impedance, parasitic effects, noise and signal integrity. Characterise and compare different
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near-field optical microscopy, investigating molecular vibrations, phonons and unusual light–matter coupling regimes. Depending on the candidate’s interests, there will also be opportunities to develop
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to accurate sample reconstructions using advanced signal processing and tomographic reconstruction algorithms. With the inclusion of noise the object estimation accuracy will be based on statistical concepts
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the integration of advanced sensing technologies, digital twins, and Internet of Things (IoT) infrastructures. In acoustics, monitoring technologies have traditionally focused on environmental noise monitoring and
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POWER project addresses this challenge by filling vital knowledge gaps regarding underwater noise, biomass and water flows, and wildlife disturbance (such as seabirds and bats) to support a sustainable
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an error, GNSS-IR analyses variations in signal-to-noise ratio (SNR/CN0), phase and related observables caused by interaction of the GNSS signal with the land surface. These reflected signals contain
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image data. The research explores how AI-driven analysis can move beyond manual reverse-engineering workflows by automating feature extraction and structural interpretation while remaining robust to noise
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to cases with low signal-to-noise ratio and low-power operation. A first research direction will focus on investigating innovative energy harvesting techniques aimed at powering sensors without the use
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test whether granular vibrations – “granular heating” – are what weakens these flows and lets them run so far. Working with Dr Eric Breard in the School of GeoSciences and with international partners
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observations of sub-Neptune exoplanets, with a focus on the most promising water world candidates. Develop analysis techniques to disentangle planetary signals from instrumental artefacts and stellar noise