Computational Biologist

  • Location

    Cambridge, Cambridgeshire

  • Sector:

    Life Sciences

  • Job type:

    Permanent

  • Salary:

    Negotiable

  • Contact:

    Janne Bate

  • Contact email:

    Janne.Bate@volt.eu.com

  • Job ref:

    77723-PHARM-JNB_1610722611

  • Published:

    9 months ago

  • Expiry date:

    2021-03-16

  • Start date:

    22/6/20

Reporting to Senior Scientists the Computational Biologist will be joining a highly collaborative team with diverse skill sets who work together to lead decisions and analysis in discovery and characterisation of reprogrammed cell lines.


Role:

Contributing to the design and execution of research programmes for uncovering core transcriptional programs that define human cell types.
Performing computational analyses and reporting of results.
Development of tools to facilitate data interpretation by laboratory scientists.
Working collaboratively with the wider computational biology team and experimental stem cell scientists under supervision of senior scientists.

Requirements:
PhD in computational biology or a related biological field with a strong bioinformatics component.
Demonstratable experience with analysis of large high-throughput sequencing datasets (esp. bulk RNA-seq and/or single cell RNA-seq).
Good knowledge of bioinformatics tools, resources and public databases.
Solid biological knowledge and interest, and familiarity with laboratory experimental design.
Proficient in R and/or python.
Working knowledge of statistical approaches in data science.
Ability to clearly communicate requirements for and results of bioinformatics analyses to wet lab biologists.

Ideally
Experience of working with ChIP-seq, ATAC-seq and/or other genomic technologies.
Familiarity with gene regulatory networks, mechanisms of gene expression, developmental biology, and stem cell biology.
Experience in the analysis of whole-genome sequencing data.
Oxford Nanopore sequence analysis.
Experience with cloud computing (preferably AWS; Nextflow a plus).
Experience in development of NGS analysis pipelines for large numbers of samples.
Familiarity with version control systems for code reproducibility.
Experience in the development of tools that help experimental biologists visualise and interrogate data.