97 data-"https:"-"https:"-"https:"-"https:"-"UNIV" Postdoctoral positions at University of Oxford
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on patient perspectives and aims to understand the information older people need about the harms and benefits of prostate specific antigen (PSA) testing (ProsDetect II). The second project addresses challenges
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applications. This will include applying expertise in inorganic polymerisation catalysis and organic functional group chemistry, analysing experimental data, and working closely with project partners. The role
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of human samples collected as part of this clinical trial must comply with the Medicines for Human Use (Clinical Trials) Regulations 2004 and will provide the primary endpoint data that will dictate
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of extension by 1 year. This project involves searching new particles using the data from the ATLAS experiment at the Large Hadron Collider. The successful candidates will analyse data from the ATLAS experiment
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and interpreting data, and adapting methodologies where required. The role also involves preparing manuscripts for publication, presenting research at conferences and scientific meetings, and
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experimental protocols; test hypotheses and analyse scientific data from a variety of sources; contribute ideas for new research projects; develop ideas for generating research income, and present detailed
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@eng.ox.ac.uk) For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/ Only online applications received before midday on Monday 20th April 2026 can be considered. You will
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research income generation. You will also have experience in working with large micro databases and grouped data. Experience of independently managing a discrete area of a research project, working in
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cell-based assays. You will possess strong experimental design and data analysis skills, with the ability to independently plan, execute and troubleshoot complex experiments. You will have highly
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identification of phases in metallic systems such as aluminium alloys or steels. You will have demonstrated expertise in applying machine learning and computer vision techniques for the analysis of scientific