The University of Manchester
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
Aga Khan University
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
MPOWIR Mentoring Physical Oceanography Women to Increase Retention
Royal Devon University Healthcare NHS Foundation Trust
The University of Manchester
University of Southampton
University of Oxford
NHS University Hospitals of Liverpool Group
University of Cambridge
University of Essex
University Academy 92 (UA92)
University of Cambridge
A leading UK university in Manchester is seeking a Biomedical Data Scientist specializing in Spatial Bioinformatics. The role involves applying computational methods and machine learning for spatial omics and data integration. Candidates should hold a PhD with experience in computational analysis and a strong interest in biology. The position offers an attractive benefits package and the opportunity to work in a cutting-edge research environment.
Applications are invited for the post of Biomedical Data Scientist specialising in Spatial Bioinformatics. The post is based in the new Medical Research Council Centre of Research Excellence (MRC CoRE) in Exposome Immunology, to be hosted jointly by the universities of Manchester and Oxford. The CoRE will leverage cutting edge computational approaches, novel experimental models, and experimental medicine studies to uncover how pollutants and infections interact with our genes to cause and exacerbate chronic inflammatory diseases.
The post-holder will be responsible for applying computational methods for analysis of data from high-resolution spatial technologies, including spatial transcriptomics and proteomics data integrated with clinical data and other high-resolution omics datasets. They will apply cutting-edge machine learning methods for spatial omics and for multi-modal data integration. The post-holder will also collaborate on the development of new computational methods to support the CoRE's scientific aims, published as well-documented open source tools for wider adoption.
The successful applicant will have a PhD with a significant computational and/or statistical element and will have experience of machine learning. A strong interest in biology and bioinformatics is essential. The candidate should also have a good scientific publication record given career stage and be self-motivated, hard-working and able to work in a team.
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