Research Fellow in Computational Biology

University of South Hampton

Southampton

On-site

GBP 39,000 - 52,000

Full time

3 days ago
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Benefits offered by this job

Generous holiday allowance
USS pension scheme
subsidised health & fitness
Discounts

Job summary

Cyber Security Academy Southampton seeks a Research Fellow in Computational Biology to advance GRN inference in single-cell genomics within the BioFAIR Pathfinder project. You will curate datasets, containerise workflows, and benchmark GRN tools as part of a national community resource.

You will join an interdisciplinary team at Southampton, with access to IRIDIS HPC and British FAIR data collaborators, contributing to publications and training opportunities.

Qualifications

  • Experience in single-cell bioinformatics and gene regulation analysis.
  • Background in quantitative sciences with strong data analytics.
  • Ability to develop, benchmark and test analytical methods.

Responsibilities

  • Develop computational framework for GRN inference in single-cell genomics.
  • Curate and harmonise single-cell and multimodal datasets.
  • Containerise workflows and compare GRN inference tools as a community resource.
  • Contribute to peer-reviewed publications and community engagement.

Skills

Computational biology
Single-cell genomics
Data-driven methods
GRN inference knowledge
RSE background

Tools

Containerisation (Docker/Singularity)
Workflow development
HPC experience (IRIDIS)

Job description

Research Fellow in Computational Biology

The Role:

We are seeking an outstanding computational biologist to join the BioFAIR Pathfinder project to develop computational framework for gene regulatory network (GRN) inference methods in single-cell genomics.

The project involves curating, harmonising single-cell and multimodal datasets, containerising and running, comparing GRN inference tools as a community resource. The role includes contributing to peer-reviewed publications and developing community, training and engagement opportunities with BioFAIR and ELIXIR-UK Southampton node.

You will join the collaborative interdisciplinary environment within the School of Biological Sciences, working alongside a growing computational genomics community at Southampton. You will have access to IRIDIS HPC, with support from RSEs, and also collaborate nationally through BioFAIR project and ELIXIR-UK Southampton Node.

About you:

You will have a quantitative or RSE background and an interest in applying computational and data-driven methods to single-cell genomics and gene regulation. We are looking for someone who can work confidently with large and varied single-cell datasets, and who enjoys developing, benchmarking, and testing new analytical approaches.

Experience in single-cell bioinformatics, containerisation, and workflow development will be critical to the role, and we are interested in your ability to think clearly, learn new methods, and work well with others. The Pathfinder project has a community engagement aspect and will require someone who is comfortable engaging with researchers from different disciplines and institutions across UK and FAIR data community.

Alongside the project, the role comes with benefits that will make your life easier, such as a generous holiday allowance and additional university closure days. As a university employee, you will have access to the Universities Superannuation Scheme (USS)*, subsidised health and fitness facilities, and a range of discounts.

This post is offered on a fixed-term basis for 12 months.

As a university we aim to create an environment where everyone can thrive and are proactive in fostering a culture of inclusion, respect and equality of opportunity. We believe that we can only truly meet our objectives if we are reflective of society, so we are passionate about creating a working environment in which you are free to bring your whole self to work. With a generous holiday allowance as well as additional university closure days we are committed to supporting our staff and students and open to a flexible working approach.

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