An exciting opportunity is available for an enthusiastic post graduate candidate to develop and implement bioinformatics capabilities required to utilise genomics into a successful duck breeding programme.
The role is available as part of the Knowledge Transfer Partnership scheme between the Roslin Institute, (the University of Edinburgh) and Cherry Valley Farms Ltd (CVF). The Associate will be employed by the University but based at CVF, in Lincolnshire.
Cherry Valley Farms is the foremost supplier of breeding stock to the Pekin duck industries around the World, with breeding bases in the UK, China and Germany. The company has over 60 years of experience and innovation in genetic research & development providing continual improvement of Pekin meat duck. The company supervisor will be Dr Anne Rae, who has 24 years’ experience in quantitative genetics and 8 years duck breeding.
The Roslin Institute is a world leader in animal research. The academic supervisors are Dr Kellie Watson and Dr Andreas Kranis. Kellie has worked in both academic and industrial backgrounds, with over 20 years’ experience in animal breeding, genetics and genomics. Andreas will be co-supervisor. Over the past 12 years he has worked closely with Kellie and brings bioinformatics/data science expertise to the project. Together they have been key in implementing genomics in poultry breeding programmes.
The work builds on an Innovate UK funded project to implement genomic parental assignment and assess the value of genomic selection in the CVF breeding programme.
The post will involve: A review of company business and key stages for genomic information; Data storage and curation; Development of a working data flow for genomic evaluation; Development of an automated workflow for genomic evaluation; Quality control & data metrics; Development of automated reporting for Genomics & breed development; Implementation & commercialisation.
Candidates should have Degree in Computer Programming and MSc/PhD (or near completion) in bioinformatics. Experience is required in data analysis/scripting programming languages (e.g. R, python or equivalent); Working with large data sets; Analysing high throughput genomic data, including sequencing data; Genomic prediction and Genome Wide Association Studies; Working with SNP arrays.
Candidates should have demonstrable ability to work independently and as part of a team; Ability to communicate complex concepts effectively both orally and in writing, including a relevant peer-reviewed publication track record and attendance at major conferences.
To apply, please submit your CV and covering letter giving evidence of how you fit the candidate criteria.
This post is available on a fixed term basis for 24 months.
The role is available as part of the Knowledge transfer partnership scheme. The Associate will be employed by the university but work within Cherry Valley Farms (CVF), Lincolnshire to develop robust automated bioinformatics capabilities to enable CVF to implement genomics into a their successful duck breeding programme.
Approx. % of time
- Develop bioinformatics pipelines to enable genomic data storage, curation and reporting.(20%)
- Implement imputation pipeline for genomic information (15%)
- Evaluate alternative genomic evaluation methods for genomic breeding value estimation and implement workflow.(30%)
- Implement quality control measures for genomic evaluation (20%)
- Contribute toward ongoing collaborative projects between CVF and RI (10%)
- Undertake appropriate training and development associated with the KTP Associate programme (5%)
Planning and Organising
The Associate will have responsibility for overall day-to-day management of the KTP Associate programme and will be expected to manage a programme of research agreed with the line manager (Anne Rae (AR), CVF and Kellie Watson, RI) in line with the responsibilities in the table above and according to an agreed timetable. The Associate must liaise with other members of Kellie Watsons (KW) and Andreas Kranis’s (AK) research groups. The Associate is expected to fully integrate into the R&D group at CVF, Lincolnshire.
The Associate is expected to resolve most problems without assistance from the line manager, although the line manager and collaborators will be available to give advice. As part of the KTP scheme the associate will have regular, close contact with KW, AK and AR. There is a generous training budget available associated with the post and the Associate will be encouraged to capitalise on this to develop their problem solving skills.
- Taken independently: Organise own workload according to priority and adapt as necessary, meaning that good time and workload management skills will be critical. The post holder will normally be expected to make operational and research decisions autonomously.
- In collaboration with others: Problem solving and data interpretation, computational workflow design and discussion of short-term research aims and potential new research strategies. All to be discussed with the academic and industry partners as necessary.
- Referred to line manager: Overall timelines and milestones, major project issues and changes of direction, major health and safety issues, major financial issues.
- Level of direction given: The academic and industry partners will offer general orientation by providing a project plan outlining the key research goals and activities that need to be completed. From there, the post holder will take responsibility for day-to-day activities but with regular meetings with the KTP management committee update on progress and discuss the research.
The day-to-day management of the Associate will be Anne Rae from Cherry Valley, Lincolnshire (the company supervisor). However, there will be a close working relationship (weekly meetings) with the Lead academic/academic supervisor: Kellie Watson, and the Support Accademic: Andreas Kranis. The job will entail trips to meetings and conferences, including regular quarterly management meetings held at CVF, Lincolnshire.
Knowledge, Skills and Experience Needed for the Job
- M.Sc. or Ph.D (or near completion) in quantitative genetics, bioinformatics or data science
- Excellent programming skills in common programming languages such as Python, R, Java, C++, etc
- Understanding of genomic data including, sequence, SNP data etc.
- Demonstrable ability to work independently and as part of a team
- Ability to communicate complex concepts effectively both orally and in writing
- Excellent communication skills and experience of working in collaborative scientific projects
- Experience and knowledge of animal or plant breeding
- Experience of working within an interdisciplinary research environment
The Associate will be based at the R&D headquarters of Cherry Valley Farms, Lincolnshire and will be under immediate supervision of Anne Rae. The Academic supervisors are part of the Genetics and Genomics Division at the Roslin Institute, University of Edinburgh.
Job Context And Any Other Relevant Information
The Associate post forms the basis of a collaborative project for the development and implementation of automated bioinformatics capabilities to create the data pipelines required to implement genomics into a duck breeding programme funded under the Knowledge Transfer Partnerships scheme.
A ‘Knowledge Transfer Partnership’ Enables Companies Of Any Size To Access Additional Scientific, Engineering Or Management Knowledge And Expertise From Universities And Research Organisations And To Import New Techniques And Technologies Into Their Businesses To
‘Knowledge Transfer Partnerships’ are an extension of the highly regarded and long established TCS scheme, which is funded by the InnovateUK with 17 other funding organisations.
- Improve existing products
- Develop new products
- Streamline a manufacturing process;
- Improve logistics processes; or
- Strengthen strategic marketing capabilities
The role is grade UE07 and attracts an annual salary of £33,797 to £40,322 for 35 hours each week. Salary is paid monthly by direct transfer to your Bank or Building Society account, normally on the 28th of the month. Salaries for part-time staff are calculated on the full-time scales, pro-rata to the Standard Working Week.
This role is grade UE07and therefore the post holder is automatically included in membership of the Universities Superannuation Scheme (USS), subject to the USS membership criteria, unless they indicate that they choose not to join the Scheme.
For further information please visit our pension’s website: http://www.ed.ac.uk/schools-departments/finance/pensions/scheme-details/uss
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Closing date: 15 October 2020 at 5pm.
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