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Interview Date: To be confirmed Reference: CSS-0224-25 We are looking for enthusiastic individuals to apply for a two year fixed term fellowship in Cardiothoracic Surgery, in the Department of Clinical
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, relevant experience in computer-based statistical analysis and presentation of results, demonstrated proficiency in a coding language used for data analysis, such as Python or R, strong quantitative skills
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subject area for the research programme is essential. Demonstrable experience in computational biology, computer science, statistics or related field. Essential experience in scripting languages to support data
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of pediatric brain tumours (Vinel et al BMC Biology 2025, Constantinou et al Cell Reports 2024 and Vinel et al. Nature Communications 2021) to develop new personalised therapies. About You We seek an ambitious
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statistical and machine learning to real life problems, using a popular computer language (e.g. Matlab, Python), and familiarity with topological and geometric data analyses. Candidates will have excellent
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. Fluent English is required along with some proficiency in German and/or French. The postholder will join an internationally oriented team of scholars and cultural sector partners mobilising archival
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identities. Your focus will be on the sixteenth to eighteenth century section of the specification, with key topics being the Pilgrimage of Grace, the English Civil War, and the American Revolution. You should
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required. Given the collaborative nature of this project, a willingness to travel and engage with industrial partners is advantageous. About the School/Department/Institute/Project Based at the School
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the Euclid satellite, we investigate fundamental questions about the nature of celestial objects, the evolution of galaxies, and the structure of the Universe itself. We are also actively involved in
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consent from patients undergoing surgery for use of their ‘waste’ surgical tissue, prior to surgery. Additional understanding and experience in maintaining all documentation needed under the relevant Human