Senior Bioinformatician in Single-cell/Spatial Multi-omics and Machine Learning/AI

University of Oxford, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences; Oxford; On-site; GBP 40,000 - 50,000; Full time

Job summary

An esteemed research institution is seeking a Senior Bioinformatician specializing in Single-cell/Spatial Multi-omics and Machine Learning/AI. This role involves analyzing large-scale datasets, developing computational pipelines, and collaborating with academic and industry partners. Ideal candidates will have a PhD/DPhil with a strong computational focus, expertise in bioinformatics, and proficiency in Python. This is a full-time, fixed-term position for three years, offering the opportunity to contribute to innovative drug therapies.

Qualifications

• PhD/DPhil with significant computational and/or statistical element or comparable industrial research experience.
• Expertise in bioinformatics analysis of omics datasets.
• Experience applying ML/AI methods and knowledge of Python programming.
• Experience developing computational methods for omics data analysis.
• Knowledge of another relevant programming language (e.g., R) is desirable.

Responsibilities

• Lead management and analysis of large-scale datasets for GSK-Oxford.
• Develop and maintain computational pipelines for omics analysis.
• Implement/develop ML/AI approaches for key questions.
• Develop new methodologies for integration of omics data.
• Work closely with partners to address scientific hypotheses.
• Provide bioinformatics input into grant proposals.

Skills

Bioinformatics analysis of omics datasets

Machine learning/AI methods

Python programming

Computational methods for omics data analysis

Education

PhD/DPhil with computational/statistical focus or industrial experience

Tools

R programming

Job description

Position Overview

We are looking for a Senior Bioinformatician in Single-cell/Spatial Multi-omics and Machine Learning/Artificial Intelligence to join the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences at the University of Oxford. The Experimental Medicine Collaboration (EMC) will combine GSK's drug discovery, target validation and clinical development with Oxford’s research and development of new drug therapies and Oxford’s Old Road Campus Experimental Medicine capabilities, particularly the Experimental Medicine Clinical Research Facility (EMCRF).

Key Responsibilities

• Lead the management and analysis of large-scale datasets to address joint GSK‑Oxford hypotheses, working with academic, clinical and industry colleagues.
• Develop and maintain computational pipelines and workflows for single-cell and spatial omics analysis and integration with clinical, demographic and experimental data.
• Implement or develop ML/AI approaches to address key questions.
• Develop new methodologies and take overall or shared responsibility for development/maintenance of computational pipelines for integration of omics data with other relevant datasets.
• Work closely with experimental, clinical and industry partners to better understand the domain, help address scientific hypotheses and support programme objectives.
• Provide bioinformatics input into outline and full grant proposals, as a co‑applicant.

Required Qualifications

• PhD/DPhil with a significant computational and/or statistical element, or comparable industrial research experience.
• Expertise in bioinformatics analysis of omics datasets, including single‑cell, spatial multi‑omics and single‑cell immune repertoire analysis.
• Previous experience applying machine learning/AI methods, with knowledge of Python programming.
• Experience developing computational methods for omics data analysis.
• Experience and knowledge of another relevant programming language (e.g., R) is desirable.

Employment Details

This is a fixed‑term, full‑time position for 3 years, in the first instance.

Security Checks

• A satisfactory basic Disclosure and Barring Service check.

Application Process

The closing date for this position is 12 noon on 22 December 2025. Applicants must upload a CV and supporting statement as part of their online application.

Please note, due to departmental closure over the Christmas period, shortlisting and interviews for this position will take place in January 2026.