Research Assistant: (5) £24,774 - £28,695
Research Associate (7): £28,695 - £37,394
Senior Research Associate (9)*: £38,511 - £48,743
We seek to recruit ambitious talented statisticians who are interested in applying their skills in the genetics/genomics field to world-leading large-scale studies of cardiovascular disease at the Cardiovascular Epidemiology Unit (CEU), Department of Public Health and Primary Care. This position can be filled by an appropriate candidate at Research Assistant, Research Associate or Senior Research Associate* level depending on relevant qualifications and experience.
The successful candidate will be involved in statistical analyses of cardiovascular outcomes and risk factors using genetic and ‘multi-omics’ (eg metabolomics, lipidomics, proteomics, transcriptomics) data derived from high throughput assays. The aims of this work are to identify causal variants and pathways for cardiovascular diseases, and ultimately inform drug discovery programmes and translate findings into clinical practice. The post-holder will apply and develop as necessary statistical methods for analyses of the Unit's vast portfolio of genetic data, expected to lead to first author high-impact publications. Currently this data comprises ~150,000 participants with extensive information on many cardiovascular risk factors and genetic data (currently ~1 million common and rare variants) measured using various microarrays and through next generation sequencing.
In addition, the post-holder will be encouraged to design and implement analyses of genetic datasets from large consortia in relation to cardiovascular disease and its risk factors. Specific examples of these projects may include (but are not limited to):
- Integrating multi-omics data to answer questions of clinical relevance.
- Analyses of common, low-frequency and rare variants in population-based studies of discrete and quantitative traits to discover novel genetic associations;
- Fine-mapping of gene regions of interest;
- Pathway analysis methods integrating data from both rare and common variants;
- Mendelian Randomisation approaches to assess causality and inform drug discovery;
- big data analysis methods
Applicants should hold a relevant post-graduate statistical training e.g. PhD in Statistics, Statistical Genetics, Medical Statistics, Biostatistics, Mathematics, or similar including those who have submitted but not yet received their PhD; have a sound understanding of statistical concepts; experience of manipulating and managing large datasets; strong quantitative (in silico) analysis skills; experience of statistical method development and experience of using relevant statistical software, either Stata or R.
It would be advantageous if applicants also had a working knowledge of genetics, preferably in relation to chronic disease epidemiology; experience of analysing large, multi-dimensional datasets (eg, GWAS, Exome sequencing data, NGS data, RNAseq, `omics’ data); previous scripting experience e.g. in Perl, Python and programming experience, e.g. C/C++/Java; experience of working in Linux-based operating systems including computer clusters; a track record of authoring scientific publications; understanding of methods for complex/integrative analyses (e.g. Mendelian Randomisation); be able to work independently judging priorities and also have excellent organisational and communication skills.
The selected candidate will join the Genetics Team at the CEU and will work with an interdisciplinary team of scientists, bioinformaticians and statisticians at the CEU, and scientific collaborators based in other institutions.
*NB: The status of Senior Research Associate is awarded on the basis of individual merit and may be awarded to the successful candidate if approved by the Faculty Board of the Cambridge School of Clinical Medicine
The funds for these posts are available until 31st August 2018 in the first instance.
Informal enquiries can be made to Dr Joanna Howson by email ([log in to unmask]) or telephone + 44 (0) 1223 748661.
Location of post: CEU, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN
Closing date: 26th April 2015
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