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Post-doctoral Research Fellow

The University of Edinburgh - CMVM / Human Genetics Unit

Location: Edinburgh
Salary: £39,347 to £46,974 per annum
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 13th August 2024
Closes: 26th August 2024
Job Ref: 11066

Fixed Term Contract - Temporary.

Full Time - 35 Hours Per Week.

The Opportunity:

This is an exciting opportunity to build-on and complete an established and well framed project investigating the earliest stages of cancer. It involves the innovative integration of transcriptional and mutation data to effectively look back in time at the past transcriptional states of cells that subsequently grew into tumours. The project involves hands-on work with animal models, molecular biology and high-throughput computational analysis of the resulting data. There is a clear framework for the completion of the project and scope to define new work building on the initial set of findings. The post-holder will join an enthusiastic collaboration between the Taylor and Boulter labs at the Institute of Genetics and Cancer (IGC). The Taylor lab specialises in high-throughput computational analysis to understand the mechanisms of mutagenesis and the consequences of mutations. The Boulter lab focusses on the cellular and molecular mechanisms of liver development, repair and oncogenesis. Both labs are based in the MRC core-funded MRC Human Genetics Unit which is part of the wider IGC.

This post is full-time (35 hours per week), however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.

Your skills and attributes for success: 

  • Hold or pending award of PhD or equivalent in cancer genomics or a related discipline (Desirable).
  • Trained in animal handling and in possession of a relevant Personal Licence (PIL) (Desirable).
  • Experience in genome scale analysis using R (Desirable).
  • Demonstrated ability in experiment design.
  • Knowledge of gene expression analysis for single-cell or similar data.
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