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Senior Research Associate in Genetic and/or Molecular Epidemiology/Population Health Data Science
4 months ago
The role
We are seeking an enthusiastic postdoctoral researcher with experience in genetic/molecular epidemiology to join the MRC Integrative Epidemiology Unit (MRC IEU) at the University of Bristol.
The post holder will work with research teams worldwide as part of the MRC funded Diverse Epigenetic Epidemiology Partnership (DEEP) study. The postholder will analyse genome-wide DNA methylation and genetic datasets with the aim of determining causal relationships between DNA methylation and complex traits in different ancestries.
You will be joining a world-class research team at a leading institution. The molecular epidemiology group of the MRC IEU is a dynamic group with a strong portfolio of grant-funded projects. You will be encouraged to contribute your own ideas, take responsibility for discrete project elements and you will be keenly supported in achieving your career development goals.
The MRC IEU research environment is truly interdisciplinary and highly collaborative. The MRC IEU has expertise spanning methodological, applied and translational research across diverse areas such as cardiovascular disease, cancer, perinatal health and mental health.
This role is based in Oakfield House, Clifton, Bristol and there is an operational requirement to work on-site for a proportion of the working week. If desired and with suitable arrangements in place, it is possible to fulfil part of the role remotely. Funding is available until 9/3/2028.
What will you be doing?
You will work on the development and application of new approaches (interaction methylation QTLs, non-disclosive methods) to capture genetic, environmental and ancestry related differences in DNA methylation variation across our cohort network. You will build an open-source multi-ancestry mQTL analysis pipeline and data resource for the academic community. To identify potential target mechanisms, you will use Mendelian Randomization to determine which associations have causal evidence. The role will involve statistical analysis of ancestrally diverse datasets, writing scientific papers within a collaborative team, presenting data at conferences and working collaboratively within an international network of partners.
You should apply if
You have:
- A strong background (PhD or postdoctoral research) in genetic/molecular epidemiology or statistical genetics or population health science
- Experience using statistical programming languages (such as R and python)
- Ability to work with large-scale molecular datasets
- Experience of applied statistical analysis, preferably with understanding of genome wide association studies and Mendelian randomization methods
- Ability to manage research projects and work collaboratively with international research teams
- Evidence of excellent writing skills including authorship of peer-reviewed journals (aligned with your career stage)
- Commitment to data accuracy and clear presentation of research outputs
- Commitment to developing equitable partnerships across the DEEP study
Additional information
Contract type: Open-ended with funding from 01/10/24 to 9/3/2028
Work pattern: Full-time/1 FTE, Monday-Friday (4 days per week considered)
Shift pattern: 35 hours per week (part-time considered)
This advert will close at 23:59 GMT on 30/05/2024
For informal queries please contact: Josine Min ) and Hannah Elliott )
We recently launched our strategy to 2030 tying together our mission, vision and values.
The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution.
£41,732 to £46,974 per annum pro rata if part-time (Grade J/Pathway Two)
