20 linked-data-"https:" "https:" "https:" "UCL" Postdoctoral positions at University of London
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learning to improve detection using multiphase CT imaging and longitudinal biomarker data in routine practice. You will work with multidisciplinary clinical and AI teams on applied research, developing and
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to develop their scientific career and have an impact in the cell invasion and cancer fields. More information about our research can be found here: www.bci.qmul.ac.uk/staff/dr-pedro-monteiro About You Our
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will develop the mathematical/computational tool-box to describe the evolution of ecDNA and test predictions of these theories in existing and newly generated cancer genomic data. The position will be
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the Centre for Experimental Cancer Medicine and the Barts NHS Trust to achieve improvements in cancer patient care. BCI is also a partner in the CRUK City of London Major Centre (together with UCL, Kings and
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international networks, close links to museums and heritage organisations, and an active programme of seminars, workshops, and public events. The post will also require significant amount of time spent
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funded by the British Heart Foundation and focuses on identifying novel treatments for aortic aneurysm linked to Marfan syndrome. About You This post would be ideally suited to a newly qualified PhD, or
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will develop and apply computational methods for the analysis of cell-free DNA (cfDNA) sequencing data, supporting a growing research program at the intersection of epigenomics and translational medicine
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: generating evidence to support policy. It is a collaborative project led by QMUL and the Policy, Information and Communication Directorate at Cancer Research UK (CRUK). The work will focus on exploring
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auditing, explainable AI (XAI) analysis and data-flow mapping, on welfare-AI systems in the UK, Netherlands and Sweden, contributing to comparative insights and high-quality project outputs. They will
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collaborators, you will conduct analyses of the Swiss HIV Cohort Study (SHCS) data to identify and characterise underserved populations who could benefit from PrEP. You will use intersectional approaches