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range of studies and the methodologies to be used. The post will offer opportunities to apply a variety of data sciences approaches and work with a wide range of large health/clinical/biological datasets
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range of studies and the methodologies to be used. The post will offer opportunities to apply a variety of data sciences approaches and work with a wide range of large health/clinical/biological datasets
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, functional MRI, diffusion-weighted imaging, and EEG, in combination with genetic data (GWAS, bioinformatics) from large-scale population cohorts. Positioned at the intersection of biological psychology
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transcriptomic datasets to uncover tissue organization and disease states. Develops integrative models combining genomics with pathology images or time-series data. Participates in large collaborative studies
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data collection and research plans Establishing and the management of large databases Interpretating and analysis of data, utilising an array of methods and modelling techniques Generating reports and
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well as competence in handling sensitive and confidential data. The candidate will work closely with the Principal Investigator and other team members to integrate experimental findings and theory to develop new tools
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is a large, unified department with strong undergraduate degrees, nationally ranked graduate programs, and world-class research spanning the breadth of biological questions and experimental systems
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, metabolites and other biomarkers. The successful candidate will contribute to knowledge on the mechanisms of antidepressants by analysing data from large clinical and population-based studies. The post-doctoral
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. The successful candidate will join a large and diverse group of researchers covering nuclear structure physics, nuclear astrophysics, applications of nuclear physics and nuclear theory. The candidate's programme
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Faculty of Life Sciences and Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and