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evolution. The project is about collecting data and analysing it in search for circumbinary planets. At Birmingham we search for such signals using radial-velocities, and the transit method, which means we
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of planet formation and evolution. Yet, we are still to find a true Earth twin or exoplanetary systems resembling the Solar System. Finding and characterising planets like Earth are hindered by variable
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”. The student will have opportunities to collaborate with leading international research groups at the University of Cambridge, MIT, and UCLA, and to engage with an interdisciplinary team of physicists, chemists
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experience of fieldwork alongside experienced scientists, develop knowledge of cutting-edge approaches in biostatistics and test fundamental ecological hypotheses about the mechanistic bases for the spread
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modulators of P2X7 (facilitating ion channel opening) can engage with the protein and alter downstream cell signalling pathways. We will use a newly developed method from our group called on-cell saturation
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microbes, and soil organic matter to uncover new insights into ecosystem recovery after wildfires. By understanding these interactions, we can develop practices and technologies for environmental management
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to use cutting edge techniques, developed in our lab, to design molecules which can control this protein complex. This chemical biology project will be interdisciplinary, involving training in
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will be tailored by enzymatic glycoengineering. The project will develop skills in chemical and enzymatic synthesis, purification techniques (flash chromatography, HPLC), spectroscopic (NMR) and mass
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Location: West Cambridge Funder: Tata Steel and the University of Cambridge Duration: 4 years from 1 October 2026 Supervisors: Prof Howard Stone and Dr David Collins Location: The PhD studentship
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, this project contributes to advancing smart materials diagnostics, supporting sustainability, safety, and technological competitiveness in key engineering sectors. To develop an AI-driven methodology