Postdoctoral Fellow

Are you interested in studying metagenomic derived protein families and developing methods to interrogate vast collections of proteins, to determine pockets of interesting novel families? Metagenomics is transforming our understanding of the microbial world by uncovering enormous numbers of novel proteins. MGnify, one of the largest metagenomics resources, contains ~6 billion unique protein sequences. At the same time, artificial intelligence (AI) methods like AlphaFold and ESMfold can accurately predict protein structures directly from sequence. The AlphaFold database (AFDB) contains models for >200 million UniProt proteins, while the ESM Atlas comprises >600 million models for MGnify proteins, with many more expected soon.

A new joint initiative with Christine Orengo’s team based at University College London, we will co-develop scalable strategies for classifying the MGnify protein databases into protein families based on structure and investigate the distribution of novel protein families across taxa and environments.

Your group: The Finn Research Group currently comprises two PhD students and two postdoctoral fellows. This team is closely aligned with the Microbiome Informatics team responsible for producing MGnify, the AMR portal and the microbial data in Ensembl. The Finn Research covers a range of different research themes, from computational tool development to deep dives into data driven research topics such as exploring the human skin and gut microbiomes. The tool development takes on a number of different forms, from algorithmic development to the application of emerging AI technologies. These tools are typically designed to work at scale, with a view that many of these will be utilised in the data resources produced by the Microbiome Informatics team.

Your supervisor: You will report directly to Research Group lead, Rob Finn.

Your role: You will co-develop strategies that can deal with the classification of the vast protein database provided by MGnify into protein families based on structure. This will include clustering, functional labelling and developing selection criteria for producing structures. You will help update the current MGnify database with taxonomic information, based on a range of sources from within MGnify. You will also expand the biome information, based on an emerging tool produced within the wider team. Using these pieces of information, you will conduct an investigation looking for correlations between biome and taxonomy and protein family distributions, relating this to functions. Expanding on prior research, you will undertake a specific task aimed at trying to identify bacteriophage encoded bacterial anti-defence systems and use the functional and structural classification to propose potential mode of actions. The research will undertake both methodological approaches (including the adopting of AI-based approaches) as well as data analysis at scale.

You have:

• PhD in the biological sciences, computational biology, bioinformatics, computer science or a related field, and proven research experience in a relevant field.
• A strong background and understanding in microbiology and/or metagenomics.
• Understanding of protein classification approaches and the tools that underpin them.
• Research experience dealing with large datasets.
• An eagerness to work in a highly collaborative atmosphere while still being able to work independently and to meet deadlines in a timely manner.
• Strong Python skills, with demonstrable ability to produce well documented and tested software.
• Experience with UNIX/Linux, and HPC or cloud environments.
• Motivation to work in an international team on interdisciplinary projects.
• Strong communication and interpersonal skills, with the ability to communicate effectively in English, both verbally and in writing.
• Fluency in English is essential (CEFR C2 minimum or equivalent).

You may also have:

• Knowledge about bacterial defence systems and/or bacteriophage anti-defence systems.
• Experience building computational pipelines with Nextflow, Snakemake or similar.
• Experience with relational databases (e.g. MySQL, PostgreSQL).
• Experience with software development best practices, including version control (e.g. Git), testing and code review.
• Familiarity with AI-assisted coding tools, and an understanding of how to critically evaluate, validate, and improve their outputs.
• Desire to help mentor PhD students.
• Ability to work independently and collaboratively as part of the research project group, prioritise tasks, and critically evaluate your research outputs.

Contract length: 3 year fixed-term contract

Salary: Year 1 Stipend at rate of £3,535 per month after tax but excluding pension and insurance contributions.

Professional development support: The EMBL Fellows’ Career Service provides support and guidance to predoctoral and postdoctoral fellows across all six EMBL’s sites. Working with a dedicated Careers Advisor, this invaluable service will help you to take informed decisions about your career planning both in the short and longer term. Whether your main interest is pursuing a career path in academia, exploring opportunities in industry or exploring an independent venture, the EMBL Fellows’ Career Service will provide you with a portfolio of activities and resources to help you.

Why join us: Join a culture of innovation. We are located on the Wellcome Genome Campus, alongside other prominent research and biotech organisations, and surrounded by beautiful Cambridgeshire countryside. This is a highly collaborative and inclusive community where our employees enjoy a relaxed atmosphere. We are committed to ensuring our employees feel valued, supported and empowered to reach their professional potential. Enjoy lots of employee benefits:

• Financial incentives: Monthly family and child allowances, generous stipend reviewed yearly, pension scheme, death benefit and unemployment insurances.
• Flexible working arrangements including hybrid working patterns.
• Private medical insurance for you and your immediate family.
• Generous time off: 30 days annual leave per year in addition to public holidays.
• Campus life: Free shuttle bus to and from work, on-site library, subsidised on-site gym and cafeteria, casual dress code, extensive sports and social club activities (on campus and remotely).
• Family benefits: On-site nursery (Heidelberg & Hinxton), 10 days of child sick leave, paid maternity & parental leave, holiday clubs on campus and monthly family and child allowances.
• Benefits for non-residents: Visa and financial support to relocate if you're overseas.

What else you need to know: International applicants: We recruit internationally and successful candidates are offered visa exemptions. Diversity and inclusion: At EMBL, we believe that diverse teams drive innovation and scientific excellence. We encourage applications from candidates of all genders, identities, nationalities and/or any other diverse backgrounds. Job location: All our fellowships are based on-site (for at least part of each week). If you are living overseas, you will receive a generous relocation package to support you. EMBL is a signatory of DORA. Find out how we apply DORA principles to our recruitment and performance assessment processes here.

How to apply: To apply please submit a cover letter and a CV through our online system. Applications will close at 23:59 CET on the date shown below. We aim to provide a response within two weeks after the closing date below.

Closing Date: 27/09/2026