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application opening date: 2025-03-26T09:00:00+00:00
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comment: We invite applications for a Postdoctoral Research Assistant to join Pier Palamara’s research group at the University of Oxford. The position is funded by the Pioneer Centre for Statistical and Computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key challenges in human genomics, applying them to large-scale genomic datasets encompassing millions of sequenced individuals and high-dimensional health-related data. The project will build on the group’s recent work on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as well as methods for inferring genealogical structures such as ancestral recombination graphs (ARGs) and leveraging them to study heritable traits and human evolution. Applicants should hold a be near completion of a PhD/DPhil or equivalent in a quantitative discipline such as computer science, statistics, machine learning, statistical or population genetics, or a related field. They should have experience in developing and applying novel statistical methods to large datasets and possess strong programming skills. The specific technical expertise required will depend on the direction of the project, which may involve developing methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large- scale graph data structures. Experience in statistical and population genetics or related fields is highly desirable but not essential. For further information about the position or about the project, please contact Professor P…
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comment: We invite applications for a Postdoctoral Research Assistant to join Pier Palamara’s research group at the University of Oxford. The position is funded by the Pioneer Centre for Statistical and Computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key challenges in human genomics, applying them to large-scale genomic datasets encompassing millions of sequenced individuals and high-dimensional health-related data. The project will build on the group’s recent work on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as well as methods for inferring genealogical structures such as ancestral recombination graphs (ARGs) and leveraging them to study heritable traits and human evolution. Applicants should hold a be near completion of a PhD/DPhil or equivalent in a quantitative discipline such as computer science, statistics, machine learning, statistical or population genetics, or a related field. They should have experience in developing and applying novel statistical methods to large datasets and possess strong programming skills. The specific technical expertise required will depend on the direction of the project, which may involve developing methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large-scale graph data structures. Experience in statistical and population genetics or related fields is highly desirable but not essential. For further information about the position or about the project, please contact Professor Pi…
application closing date: 2025-05-02T12:00:00+01:00

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178739 - Job Description
value: _________________________________________________________________________ Job Description Job title Postdoctoral Research Assistant in Statistical Genomics Division Mathematical, Physical and Life Sciences Department Statistics Location 24-29 St Giles’, Oxford, OX1 3LB Grade and salary Grade 7: £40,855 - £46,913 per annum (discretionary range to £51,059) Hours Full time Contract type Fixed term (3 years) Reporting to Professor Pier Palamara Vacancy reference 178739 Research topic Development of novel statistical and machine learning algorithms for complex trait analysis in high-dimensional genomic data sets. Principal Investigator / supervisor Professor Pier Palamara Project web site https://www.stats.ox.ac.uk/~palamara/ Funding partner Danish National Research Foundation SMARTbiomed https://smartbiomed.dk/ Loya et al. A scalable variational inference approach for increased mixed-model association power, Nature Genetics, 2025, https://www.nature.com/articles/s41588-024-02044-7. Recent publications Zhang et al. Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits, Nature Genetics, 2023, https://www.nature.com/articles/s41588-02301379-x. _________________________________________________________________________ Job description Overview of the role We invite applications for a Postdoctoral Research Assistant to join Pier Palamara’s research group at the University of Oxford. The position is funded by the Pioneer Centre for Statistical and Computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed, https://smartbiomed.dk/), an international collaboration between the University of Oxford, Aarhus University, and the University of Copenhagen which combines large-scale, multimodal biomedical data with advances in statistical and machine learning methods to improve our understanding, treatment, and prevention of human disease. The successful candidate will develop novel statistical and machine learnin…
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