Bioinformatics Genomics Research Associate - Multi-Omics

The University of Manchester; Manchester; On-site; GBP 40,000 - 50,000; Full time

Benefits offered by this job

Generous employer contribution pension

29 days annual leave plus bank holiday

Ride to work and EV car scheme

Job summary

The University of Manchester invites applications for a postdoctoral research associate to join the Arthritis UK Centre of Excellence in Genetics and Genomics on a Medical Research Council funded project. The role focuses on characterising non-coding genome regions linked to osteoarthritis using functional genomics.

You will apply statistical and bioinformatic analyses to large patient cohorts (Hi-C, CUT&RUN, ATAC-seq, RNA-seq, single-cell RNA-seq) and develop pipelines for multi-omics

Qualifications

• Hold, or be about to obtain, a PhD in functional genomics or bioinformatics.
• Strong background and theoretical knowledge of functional genomics, epigenetics and gene regulation.
• Proven experience in genomics data analysis (Hi-C, ATAC-seq, RNA-seq and CUT&RUN).
• Experience with Linux, Python and R.

Responsibilities

• Carry out statistical and bioinformatic analysis for high-throughput genomics experiments.
• Develop pipelines for multi-omics data integration and functional annotation of GWAS variants.
• Develop functionally weighted polygenic risk scores (PRS).
• Plan work efficiently and multitask across projects.
• Contribute to academic output including abstracts, presentations and publications.

Skills

Genomics data analysis

Hi-C

ATAC-seq

RNA-seq

CUT&RUN

Python

R

Linux

Education

PhD in functional genomics or bioinformatics

Tools

Linux

Python

R

Job description

The University of Manchester invites applications for a postdoctoral research associate to join the Arthritis UK Centre of Excellence in Genetics and Genomics on a Medical Research Council funded project. The role focuses on characterising non-coding genome regions linked to osteoarthritis using functional genomics.

You will apply statistical and bioinformatic analyses to large patient cohorts (Hi-C, CUT&RUN, ATAC-seq, RNA-seq, single-cell RNA-seq) and develop pipelines for multi-omics