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System Interferometric Reflectometry (GNSS-IR) can be combined with satellite Earth observation and land-surface modelling to produce more accurate, spatially detailed and temporally frequent soil moisture
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and apply machine learning and deep learning models to analyse complex, multi-dimensional datasets, including spatial transcriptomic, proteomic, and single-cell sequencing data. The candidate will
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cross-disciplinary and focussed on translating technological advances in biomedical engineering towards improving outcomes for cancer patients. PhD students within the group are supported by postdocs
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determined by combining the observed space density of galaxies, the measured spatial distribution of galaxies and simulations of the dark matter distribution. Example themes for student projects follow and
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-bandwidth continuous magnetic sensing of an ensemble of electric spins Developing a spatially sensitive optical magnetometer catheter probe Ultra-sensitive zero-field using quantum anti-crossings web page
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spatial and temporal dimensions Quantitative functional lung imaging using near field X-ray speckle Ultra-low dose Computed Tomography for lung, brain or breast imaging. Dark field X-ray imaging Translating
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to anthropogenic N2O emissions. 2. What the environmental conditions are that balance N2O production and uptake in artificial aquatic ecosystems. 3. Spatial and temporal uncertainties associated with aquatic N2O
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. I am also interested in interdisciplinary research with a machine learning foundation. So if you’re interested in projects such as applications of ML in tracking geo-spatial changes (e.g., climate
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theoretical colleagues. All research takes place within our dynamic particle physics research group with academics and postdocs, as well as graduate and undergraduate students. Some work will be purely
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known as Team COMPAS -- includes a number of amazing undergraduate and graduate students, postdocs, alumni, and other fantastic collaborators. Please contact me if you are interested in joining our group