Lecturer in Computational Genomics

Full-time: 35 hours per week 

Open-ended: permanent contract

The Opportunity:

Establishing an innovative research line as Lecturer in Computational Genomics related to reproductive disorders and genomic medicine.

This Lecturer 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.

The salary for this post is £49,559 – £60,907 per annum.

The Institute of Genetics and Cancer at the University of Edinburgh has a faculty lecturer position for Computational Genomics, to work with the Director of the Institute, Professor Joris Veltman, on reproductive genomics and genomic medicine (see institute-genetics-cancer.ed.ac.uk/research/research-groups-a-z/veltman-group). Candidates should have a demonstrable track record of innovative research and/or translation in genomics, knowledge exchange and the potential to make an outstanding contribution to furthering the Institute’s strategic goals by developing an innovative research programme.

The mission of the Institute of Genetics and Cancer is to understand development and disease by using the latest approaches in genetic, genomic, cellular and clinical science. Our focus is on research into human genetics and cancer, and on training the next generation in an outstanding environment for basic discovery science and translation.

The Institute constitutes one of the largest aggregates of human molecular genetics and biology research capacity in the UK with more than 70 Principal Investigators, 500 staff and 100 PhD students. By pooling resources and multidisciplinary skills, the Institute brings together the scientific expertise, technology and support services needed to maximise scientific discovery.

For more information about the Institute, see institute-genetics-cancer.ed.ac.uk/about

Your skills and attributes for success:

• PhD in a relevant area (or near completion)
• Relevant post-doctoral experience
• Evidence of innovative working with genomic data to improve variant detection and interpretation
• Evidence of the ability to obtain funding to support research
• Recent record of high-quality publications

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