At a Glance
- Tasks: Join a dynamic team to develop AI-based methods for protein-ligand interactions.
- Company: Be part of EMBL-EBI, a leading bioinformatics centre on the Wellcome Genome Campus.
- Benefits: Enjoy competitive salary, generous leave, hybrid work, and private medical insurance.
- Other info: Inclusive environment with excellent career growth and relocation support.
- Why this job: Make a real impact in drug discovery while collaborating with top researchers.
- Qualifications: PhD in relevant field and expertise in protein structure analysis required.
The predicted salary is between 39600 - 44300 £ per year.
We are looking for a highly motivated Computational Biologist / Bioinformatician to join the Chemical Biology Resources team at the European Bioinformatics Institute (EMBL-EBI), located on the Wellcome Genome Campus near Cambridge, as part of the Wellcome Trust funded LIGMAP project, a multi-disciplinary collaboration between University College London and EMBL-EBI. The Chemical Biology Resources team provides world-leading chemogenomics resources to the scientific community. ChEMBL is a database of quantitative small-molecule bioactivity data curated primarily from the scientific literature widely used to support drug discovery projects in industry and academia. ChEBI is a highly curated database and ontology of compounds of biological interest. The EBI is part of the European Molecular Biology Laboratory (EMBL) and it is a world-leading bioinformatics centre providing biological data to the scientific community with expertise in data storage, analysis and representation. EMBL-EBI provides freely available data from life science experiments, performs basic research in computational biology and offers an extensive user training programme, supporting researchers in academic and industry.
Your role: LIGMAP is an ambitious 4.5 year project, led by University College London, to identify the endogenous ligands of the many proteins of unknown function in the proteome using AI-based methods as well as classical machine learning. You will work closely with the PDBe team at EMBL-EBI, as well as leading researchers in structural biology and machine learning at University College London, to deliver the goals of the project. You will develop descriptors to characterise the binding site of proteins in terms of their ability to recognise ligands, and use these to assess druggability of protein binding sites.
You have:
- A PhD in Bioinformatics, Structural Biology, Chemistry, Computer Science, or a related field.
- Deep understanding of protein-ligand interactions and expertise in protein structure analysis, specifically in characterizing binding sites and assessing druggability.
- Strong foundation in cheminformatics, including analysis of molecular representations, chemical similarity, fingerprints design, and clustering techniques, among others.
- Experience with molecular docking, virtual screening pipelines, and the calculation of chemical properties to support functional annotation.
- Proficiency in Python, with hands-on experience using cheminformatics and structural biology libraries such as RDKit, Biopython, or Open Babel.
- Familiarity with structural data formats (mmCIF, PDB) and experience working with global resources like the Protein Data Bank (PDBe).
- Experience working with relational (SQL) databases and managing large-scale biological or chemical datasets.
- Familiarity working in a structured collaborative software development environment using git and GitHub/GitLab.
- Proficiency in working with Linux operating systems.
- Self-motivated with a can-do attitude and a willingness to solve complex problems in a multi-disciplinary team.
- Excellent communication and interpersonal skills, with the ambition to work effectively with international collaborators across institutions like EMBL-EBI and UCL.
- Advanced English language skills.
You might have:
- Experience using PyMOL or other 3D molecular visualization tools (ChimeraX, VMD) for the detailed analysis of protein surfaces and binding pockets.
- Experience in scripting within visualization software to automate the generation of structural descriptors.
- Experience in using AI-based methods, such as Geometric Deep Learning, Graph Neural Networks, or LLMs for structural data processing.
Salary: Grade 5 or 6 depending on experience. Salary from £3,303.40 or £3,695.61 after tax plus generous benefits. Excluding personal pension and insurance contributions.
Benefits and Contract Information:
- Financial incentives: depending on circumstances, monthly family/marriage allowance of £278, monthly child allowance of £336 per child. Non-resident allowance up to £569 per month. Annual salary review, pension scheme, death benefit, long-term care, accident-at-work and unemployment insurances.
- Hybrid working arrangements.
- Private medical insurance for you and your immediate family (including all prescriptions and generous dental & optical cover).
- Generous time off: 30 days annual leave per year, in addition to eight bank holidays.
- Relocation package including installation grant (as applicable).
- 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, child sick leave, generous parental leave, holiday clubs on campus and monthly family and child allowances.
- Contract duration: This position is a 3 year contract.
- International applicants: We recruit internationally and successful candidates are offered visa exemptions.
- Diversity and inclusion: At EMBL-EBI, we strongly believe that inclusive and diverse teams benefit from higher levels of innovation and creative thought. We encourage applications from women, LGBTQ+ and individuals from all nationalities.
Job location: This role is based in Hinxton, near Cambridge, UK. You will be required to relocate if you are based overseas and you will receive a generous relocation package to support you.
How to apply: To apply please submit a cover letter and a CV through our online system before the closing date. Applications will close on 17/06/2026.
Computational Biologist / Bioinformatician - Chemical Biology Services in Saffron Walden employer: EMBL-EBI
At EMBL-EBI, we pride ourselves on being an exceptional employer, offering a collaborative and innovative work culture that fosters professional growth in the field of bioinformatics. Located on the Wellcome Genome Campus near Cambridge, our team benefits from generous leave, comprehensive health insurance, and a supportive environment that encourages diversity and inclusion. With access to world-class resources and opportunities to engage in cutting-edge research, we empower our employees to make meaningful contributions to the scientific community.
StudySmarter Expert Advice🤫
We think this is how you could land Computational Biologist / Bioinformatician - Chemical Biology Services in Saffron Walden
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at conferences. A friendly chat can lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and code. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to bioinformatics. We can help you with mock interviews to boost your confidence!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing enthusiastic candidates!
We think you need these skills to ace Computational Biologist / Bioinformatician - Chemical Biology Services in Saffron Walden
Some tips for your application 🫡
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Make sure to highlight your passion for bioinformatics and how your skills align with the LIGMAP project. We want to see your personality come through, so don’t be afraid to let us know what excites you about this role.
Tailor Your CV:When applying, tailor your CV to showcase relevant experience in computational biology and cheminformatics. Highlight specific projects or roles that demonstrate your expertise in protein-ligand interactions and any programming skills you have. We love seeing how your background fits with our needs!
Showcase Your Technical Skills:Make sure to include any technical skills that are relevant to the role, like proficiency in Python or experience with molecular docking. We’re looking for candidates who can hit the ground running, so don’t hold back on showcasing your abilities!
Apply Through Our Website:Remember, the best way to apply is through our online system. It’s straightforward and ensures your application gets to the right place. Plus, it helps us keep track of all applications efficiently. So, head over to our website and submit your materials before the deadline!
How to prepare for a job interview at EMBL-EBI
✨Know Your Stuff
Make sure you brush up on your knowledge of protein-ligand interactions and cheminformatics. Be ready to discuss your experience with molecular docking and virtual screening, as well as any relevant projects you've worked on. This will show that you're not just a good fit on paper but also in practice.
✨Showcase Your Skills
Prepare to demonstrate your proficiency in Python and any cheminformatics libraries like RDKit or Biopython. You might be asked to solve a problem or explain how you would approach a specific task, so having examples ready can really set you apart.
✨Collaboration is Key
Since this role involves working closely with teams at EMBL-EBI and UCL, highlight your teamwork and communication skills. Share examples of how you've successfully collaborated on multi-disciplinary projects in the past, and be prepared to discuss how you handle challenges in a team setting.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the LIGMAP project’s goals or the team dynamics. This shows your genuine interest in the role and helps you gauge if it’s the right fit for you.
