Bioinformatician - Pathogen

Bioinformatician - Pathogen

EIT - Ellison Institute of Technology

Oxford, United Kingdom

The role

At EIT we are seeking an experienced and detailed orientated Bioinformatician to work shoulder-to-shoulder with senior scientists to analyse, validate and improve bioinformatics workflows that turn raw sequencing data into actionable insights. This is an ideal role for someone early in their career who is eager to develop their skills in infectious disease genomics, microbial sequence analysis and reproducible computational biology, while seeing their work contribute to products with real-world clinical impact.

The role will involve working with a range of next-generation sequencing datasets, including pathogen-specific and metagenomic data. You will work closely with wet lab scientists to apply analytical approaches such as microbial profiling, taxonomic classification, and antimicrobial resistance analysis to experimental data and interpret the results. We are particularly interested in candidates who have practical exposure to metagenomic methods and are keen to deepen that expertise in a translational research environment.

Key Responsibilities

• Develop, apply and improve reproducible bioinformatics workflows for the analysis of whole genome and metagenomic sequencing data, including quality control, assembly, taxonomic classification, antimicrobial resistance prediction, variant analysis and strain characterisation;
• Support interpretation of complex sequencing datasets, assessing the suitability of analytical approaches and accounting for factors such as contamination, mixed organisms, host background, low microbial biomass and ambiguous taxonomic assignments;
• Use scripting and good programming practices to develop clear, reliable and reproducible bioinformatics solutions, and actively support colleagues by sharing expertise, reviewing code and contributing to collaborative problem-solving;
• Stay current with advances in infectious disease genomics, metagenomic analysis, antimicrobial resistance analytics, and microbial bioinformatics, suggesting incremental improvements to existing workflows;
• Engage in a collaborative, inclusive and high-performing team environment by sharing knowledge, supporting colleagues and contributing to continuous improvement.

Requirements

Essential Knowledge, Skills and Experience:

• Doctorate-level expertise (PhD or equivalent) in Bioinformatics, Computational Biology, Microbiology, Genetic Epidemiology, Genomics or a related field, combined with hands-on bioinformatics or computational biology experience;
• Hands-on experience analysing next-generation sequencing data, ideally in metagenomics, microbial genomics, infectious diseases, or a related field;
• Understanding of metagenomic analysis principles and the challenges associated with interpreting sequencing data from complex microbial communities, such as distinguishing co-occurring organisms, contamination, host/background signal, reference database limitations, taxonomic resolution and false-positive results;
• Strong programming skills in Python and Bash, with experience developing reproducible bioinformatics scripts, pipelines and data analysis workflows; familiarity with R is desirable;
• Exposure to workflow management systems (e.g. Nextflow) and container technologies (e.g. Docker), with an understanding of their role in developing reproducible bioinformatics workflows;
• Clear communicator, curious learner, and team-oriented problem solver.

Desirable Knowledge, Skills and Experience:

• Experience working in infectious diseases diagnostics with techniques such as clinical metagenomics or targeted sequencing;
• Exposure to cloud computing platforms (e.g. OCI, AWS or GCP) and/or high-performance computing (HPC) environments (e.g. Slurm) for running and managing bioinformatics analyses;
• Experience using version control (e.g. Git) to develop, maintain and share reproducible bioinformatics code and workflows within a collaborative team;
• Familiarity with short- and long-read sequencing technologies (e.g. Illumina and Oxford Nanopore Technologies) and an understanding of their respective applications, strengths and limitations;
• Interest in developing skills in data visualisation, statistical analysis or machine learning methods to support genomic data analysis and interpretation.