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Position: Analyst in Bioinformatics and Immune Single-Cell Genomics
Institution: University of Oxford
Location: Oxford, Oxfordshire, United Kingdom
Duties: You will conduct detailed analysis of multi-modal single-cell data and other next generation sequencing and imaging datasets generated; using analysis to identify, define and plan new projects that contribute to the overall aims of the project. You will devise novel approaches for data analysis, develop informatics structures to manage large datasets, and contribute to code and data sharing and deposition via appropriate portals, as well as keeping abreast of advances in the field, disseminating these to the team and implementing them as required
Requirements: You will hold a PhD in Immunology, Bioinformatics, Statistics, Mathematics, Computer Science or other related computational subject, have experience in the analysis of single-cell RNA-seq data (e.g. generated using 10X Genomics platforms) and be proficient in a high-level programming language. You will have the ability to supervise and train junior team members and the abibility to evaluate and implement new developments in the field. Experience using machine learning approaches for the analysis of immunological processes is desirable
   
Text: Analyst in Bioinformatics and Immune Single-Cell Genomics Kennedy Institute of Rheumatology, Roosevelt Drive, Headington, Oxford OX3 7FY We have an exciting opportunity for an Analyst in Bioinformatician and Immune Single-Cell Genomics to join Dr Calliope Dendrou’s group at the Kennedy Institute of Rheumatology. The main aim of this post will be to leverage single-cell genomics data obtained from the study participants, along with data from in vitro assays, to identify measures predictive of the magnitude and durability of vaccine-induced antibody responses. You will conduct detailed analysis of multi-modal single-cell data and other next generation sequencing and imaging datasets generated - using analysis to identify, define and plan new projects that contribute to the overall aims of the project. You will devise novel approaches for data analysis, develop informatics structures to manage large datasets, and contribute to code and data sharing and deposition via appropriate portals, as well as keeping abreast of advances in the field, disseminating these to the team and implementing them as required. You will hold a PhD in Immunology, Bioinformatics, Statistics, Mathematics, Computer Science or other related computational subject, have experience in the analysis of single-cell RNA-seq data (e.g. generated using 10X Genomics platforms) and be proficient in a high-level programming language. You will have the ability to supervise and train junior team members and the abibility to evaluate and implement new developments in the field. Experience using machine learning approaches for the analysis of immunological processes is desirable. This is a fixed-term, full time position until 31/08/2026. A lower grade offer (Grade 7: £36,024- £44,263 p.a.) may be made to a less experienced candidate (such as recent PhD/DPhil graduates) or a candidate who needs further training in one or more aspects of this position, if a suitable candidate cannot be found to fill the Grade 8 position. Due to the nature of the research at the Kennedy Institute of Rheumatology, this job will require additional security pre-employment checks: • A satisfactory basic Disclosure and Barring Service check The closing date for this position is 12 noon on Friday 21st June 2024 . You will be required to upload a CV and supporting statement as part of your online application. Contact Person : NDORMS HR Team Vacancy ID : 173070 Contact Phone : Closing Date & Time : 21-Jun-2024 12:00 Pay Scale : STANDARD GRADE 8 Contact Email : hr@ndorms.ox.ac.uk Salary (£) : £45,585 - £54,395 p.a. A lower grade offer (Grade 7: £36,024- £44,263 p.a.) may be made to a less experienced candidate , if a suitable candidate cannot be found for a Grade 8 position. Click on the link(s) below to view documents Filesize Job Description 502.5
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