Research Fellow, statistical genetics

Job Purpose

To create and contribute to the creation of knowledge by undertaking specified research activities within the established BBSRC funded project investigating the genetic basis of complex agronomic traits in the autotetraploid crop potato in the lab of Professor Zewei Luo, University of Birmingham. A position providing maternity cover is available for a Postdoctoral Research Fellow to work on a BBSRC-funded project investigating the genetic basis of complex agronomic traits in the autotetraploid crop potato in the lab of Professor Zewei Luo, University of Birmingham.

This innovative project will develop theoretical and methodological foundations for quantitative genetic analysis in autotetraploid species, including a framework for linkage mapping of Quantitative Trait Loci (QTL).

The successful candidate will have a PhD and/or postdoctoral experience in genetics using statistical approaches for quantitative genetic analysis in autopolyploid species. A keen interest in developing novel methods for genetic analysis in polyploid plants is essential, and excellent organizational and interpersonal skills are necessary. This position will be supported by a dedicated half time research technician.

This is a full-time position, fixed-term for 6 months from a start date to be agreed, and an excellent opportunity to join an innovative interdisciplinary research team.

Person Specification

• • PhD in the area of statistical genetics
• • Fluent in programming in at least one of the computer languages Fortran 90, C++ as well as in R and Perl
• • High level analytical capability
• • Experience in developing methods for genetic analysis with autotetraploid species
• • Ability to communicate complex information clearly
• • Experience in plant biology experiments
• • Fluency in relevant models, techniques or methods and ability to contribute to developing new ones
• • Ability to assess resource requirements and use resources effectively
• • Ability to design, execute and write up experimental work independently and to troubleshoot failed experiment
• • Ability to work effectively as part of a team or independently as required
• • Understanding of and ability to contribute to broader management/administration processes
• • Good time management skills, ability to prioritise and meet deadlines