Postdoctoral Fellow

embl; Hinxton; On-site; GBP 42,000 - 45,000; Full time

Benefits offered by this job

3-year fixed-term contract

Career development support

Job summary

EMBL invites applications for a postdoctoral researcher in the Finn Research Group at Hinxton. You will co-develop strategies to classify MGnify's vast protein database into families based on structure, using clustering, functional labelling, and structure-driven selection criteria.

You will update MGnify with taxonomic information, expand biome data, and investigate correlations between biome, taxonomy, and protein families, relating this to function.

Qualifications

• PhD in biological sciences, computational biology, bioinformatics, computer science, or related field.
• Strong background in microbiology and/or metagenomics.
• Experience with protein classification approaches and related tools.
• Experience handling large datasets.
• Ability to work both collaboratively and independently to meet deadlines.
• Strong Python skills with well-documented, tested software.
• Experience with UNIX/Linux and HPC or cloud environments.
• Excellent English communication skills (CEFR C2 or equivalent).

Responsibilities

• Co-develop strategies to classify MGnify's protein databases into families based on structure.
• Perform clustering, functional labeling, and develop criteria for structure generation.
• Update MGnify with taxonomic information from diverse sources.
• Expand biome information using a new tool developed by the team.
• Investigate correlations between biome, taxonomy and protein family distributions.
• Identify bacteriophage anti-defence systems and propose modes of action.
• Apply AI-based approaches and conduct large-scale data analysis.

Skills

Python

Unix/Linux

HPC/Cloud

English proficiency

Research skills

Collaboration

Education

PhD in Biological Sciences / Computational Biology / Bioinformatics / CS

Tools

Nextflow

Snakemake

Git

MySQL

PostgreSQL

Job description

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 post doctoral 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