Postdoc: Genomics & Multi-Omics for Liver Cancer Risk

Corehr; Oxford; On-site; GBP 36,000 - 48,000; Full time

Benefits offered by this job

38 days holiday

Pension scheme

Travel discounts

Professional development

Job summary

The Ansari Group at the University of Oxford seeks a Postdoctoral Research Scientist to lead the statistical analysis of large-scale genomic and multi-omic data from patients with chronic liver disease.

You will work on the DeLIVER programme, analysing genome-wide genotype data from over 2,800 individuals alongside proteomic, metabolomic and longitudinal clinical data, using GWAS, fine-mapping, QTL mapping, Mendelian randomisation, polygenic risk prediction and multi-omics integration.

Qualifications

• PhD (or near completion) in Statistical Genetics, Genetic Epidemiology, Computational Biology or related quantitative field.
• Proven experience with large-scale genomic datasets and multi-omics analysis.
• Strong programming skills in R and/or Python for data analysis.
• Experience with GWAS, population structure, imputation and related methods.
• Ability to interpret statistical results in a biological context.
• Track record of high-quality research and publications.

Responsibilities

• Lead statistical analysis of large-scale genomic and multi-omic data from patients with chronic liver disease.
• Apply GWAS, fine-mapping, QTL mapping, Mendelian randomisation and multi-omics integration.
• Collaborate across clinicians, statisticians and computational biologists within multidisciplinary teams.

Skills

R

Python

GWAS

QTL mapping

Mendelian randomisation

Multi-omics integration

Statistical genetics

Genomic data analysis

Computational biology

Education

PhD in Statistical Genetics or related

Tools

R

Python

Job description

The Ansari Group at the University of Oxford seeks a Postdoctoral Research Scientist to lead the statistical analysis of large-scale genomic and multi-omic data from patients with chronic liver disease.

You will work on the DeLIVER programme, analysing genome-wide genotype data from over 2,800 individuals alongside proteomic, metabolomic and longitudinal clinical data, using GWAS, fine-mapping, QTL mapping, Mendelian randomisation, polygenic risk prediction and multi-omics integration.