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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors
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isolation, leading to suboptimal network performance. This project seeks to address this gap by developing a holistic, system-level approach that optimises PST deployment strategies to enhance grid
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to protect our planet from asteroid hazards. The aim of the project is to deliver and validate of an onboard astrodynamics GNC software for autonomous proximity operations around minor celestial bodies
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be carried out under the UK Hypersonics Doctoral Network, which has been supported by the Ministry of Defence and EPSRC for building the necessary expertise to develop next-generation hypersonic
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and
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to reach carbon net zero by 2050. Between 40 to 50% of the carbon footprint of a litre of milk is attributed to CH4 production in the rumen (Huhtanen et al., 2022) which also has a negative impact on animal
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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provider: EPSRC DTP 25/26 Fully Funded Studentship Subject area: Algae biotechnology/computational biology/microbial community networks/ecological theory/evolutionary mechanisms/biomass productivity
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will be defined, decomposed and assessed through attention to artefacts and practices across a range of sectors and disciplines. Focusing on the automotive context, the project will develop a