Your Group
The Saez-Rodriguez group at EMBL-EBI develops computational methods that integrate prior biological knowledge with omics data to build mechanistic models of signalling networks. The group creates widely used open-source tools—including OmniPath, CARNIVAL, COSMOS, CORNETO, and decoupleR—that enable the research community and pharmaceutical partners to generate actionable, data-driven hypotheses for target identification and drug mechanism of action analysis.
The group maintains an extensive network of collaborations with academic institutions and industry, and is a core contributor to the Open Targets consortium, which brings together EMBL-EBI, the Wellcome Sanger Institute, and major pharmaceutical companies (GSK, Genentech/Roche, MSD, and others) to systematically identify and prioritise drug targets through open-access research. More information can be found at saezlab.org.
Your Supervisor
Julio Saez-Rodriguez is a Group Leader at EMBL-EBI and Professor of Computational Biomedicine at Heidelberg University. His research focuses on developing computational methods that integrate omics data with prior knowledge to build mechanistic models of signalling networks, with applications in precision medicine and drug discovery. He leads the development of widely used tools including OmniPath, CARNIVAL, COSMOS, and decoupleR, and coordinates multiple large-scale collaborative projects within Open Targets.
Your Role
You will be a software developer for the NetworkCommons project, an Open Targets–funded initiative to build a unified, open-source platform for the benchmarking and application of network contextualisation methods in drug target discovery. Network contextualisation methods integrate context-agnostic biological knowledge (protein-protein interaction networks, signalling pathways, regulatory networks) with condition-specific omics data to extract active molecular processes relevant to disease states or experimental perturbations. These methods are central to generating mechanistic hypotheses for target and biomarker prioritisation and mechanism of action analysis.
Your primary responsibility will be to design and develop the algorithmic core of the platform. This includes implementing and optimising a broad range of network contextualisation algorithms—spanning integer linear programming (ILP), shortest path and maxflow approaches, recursive propagation and diffusion methods, Prize-Collecting Steiner Trees and Forest variants, and graph clustering—with standardised inputs and outputs. A key aspect of the role will be ensuring that methods scale to large datasets, including single-cell transcriptomics and high-throughput perturbation screens, building on experience with high-performance and pre-exascale computing environments.
You will work closely with postdoctoral researchers to support benchmarking case studies across three disease areas (oncology, IBD, and neuroinflammation), ensuring that methods handle diverse omics inputs (bulk and single-cell transcriptomics, proteomics, phosphoproteomics) and integrate with existing tools developed in the group such as CORNETO, CARNIVAL, COSMOS, and phuEGO. You will collaborate with a second developer who will be responsible for the technical software infrastructure, deployment, and platform integration.
Key responsibilities:
Design and implement the algorithmic core of the NetworkCommons Python library, following open-source software engineering best practices with modular architecture, comprehensive testing, and thorough documentation.
Implement and integrate multiple network contextualisation methods, including ILP-based methods (CORNETO), propagation and diffusion approaches (Personalized PageRank, TieDie, phuEGO, COSMOSR), graph-based methods (Prize-Collecting Steiner Forest, shortest path, maxflow, network clustering), and hybrid approaches (CHARLIE, WGCNA, NicheNet), with unified standardised interfaces.
Optimise method implementations for scalability to large-scale datasets, including single-cell perturbation atlases (e.g., Tahoe-100M) and genome-wide CRISPR screens, leveraging experience with high-performance computing and mathematical optimisation.
Collect, harmonise, and organise prior knowledge networks from diverse sources (OmniPath, STRING, curated causal interactions, co-expression and functional association networks), ensuring programmatic accessibility and interoperability within the platform.
Support the development of reproducible benchmarking workflows by providing well-documented algorithmic implementations and method configurations compatible with workflow management systems (Nextflow/Snakemake).
Develop and validate new algorithmic approaches or adaptations of existing methods to address specific challenges in network contextualisation, such as handling multi-omics data integration, phenotype-aware network inference, and scalability to genome-wide perturbation screens.
Collaborate with postdoctoral researchers on benchmarking pipelines for case studies covering disease networks, perturbation response prediction, multi-omics drug response modelling, signalling mediator inference, and patient stratification.
Engage with academic and industry partners (GSK, Genentech, MSD, Sanger Institute, Imperial College London) through regular project meetings to gather requirements, incorporate feedback, and ensure the platform meets translational research needs.
Contribute to the design and evaluation of benchmarking metrics and evaluation frameworks for comparing network contextualisation methods across different biological contexts and data types.
Qualifications and Experience
Essential:
A PhD (or equivalent experience) in computer science, computational biology, bioinformatics, applied mathematics, or a related quantitative discipline.
Strong software engineering skills in Python, including experience with package development, testing frameworks, continuous integration, and documentation.
Experience with mathematical optimisation and/or algorithmic development (e.g., linear programming, integer programming, graph algorithms, constraint satisfaction).
Familiarity with network or graph data structures and associated libraries (e.g., NetworkX, igraph, or equivalent).
Experience developing and benchmarking computational methods for network analysis, causal reasoning, or related algorithmic problems in a research context.
Proficiency with version control (Git) and collaborative software development practices.
Experience working with biological or biomedical data in a research or translational setting.
Ability to work collaboratively in interdisciplinary, international teams and to communicate technical concepts to researchers from diverse scientific backgrounds.
Desirable:
Experience with causal reasoning or network inference methods for signalling pathways (e.g., ILP-based approaches, propagation methods, Steiner tree algorithms).
Familiarity with mechanistic modelling of biological systems, including metabolic or signalling network models.
Experience with high-performance or distributed computing for scaling computational methods to large datasets.
Knowledge of omics data types and their processing (transcriptomics, proteomics, phosphoproteomics), including single-cell workflows.
Experience with workflow management systems (Nextflow, Snakemake) for reproducible computational pipelines.
Familiarity with biological prior knowledge resources (e.g., OmniPath, Reactome, STRING, KEGG) and their programmatic access.
Experience with the R/Bioconductor ecosystem and interoperability between Python and R.
Track record of contributing to open-source scientific software projects.
Salary: Grade 5 - monthly salary from £3,303 after tax plus generous benefits (excluding pension and insurance contributions)
Contract Length: 3-year project limited fixed term contract.
Next Steps: This vacancy is open from Wednesday, 18th March with a scheduled closing date of Tuesday, 31st March 2026. We invite you to apply as soon as possible.
Why join us
Do something meaningful
At EMBL-EBI you can apply your talent and passion to accelerate science and tackle some of humankind's greatest challenges. EMBL-EBI, part of the European Molecular Biology Laboratory, is a worldwide leader in the storage, analysis and dissemination of large biological datasets. We provide the global research community with access to publicly available databases and tools which are crucial for the advancement of healthcare, food security, and biodiversity.
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. Watch this video to see how EMBL-EBI makes an impact.
Enjoy lots of benefits:
Financial incentives: Monthly family, child and non-resident allowances, annual salary review, pension scheme, death benefit, long-term care, accident-at-work and unemployment insurances
Flexible working arrangements - including hybrid working patterns
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 public holidays
Relocation package including installation grant (if required)
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, 10 days of child sick leave, generous parental leave, holiday clubs on campus and monthly family and child allowances
Benefits for non-UK residents: Visa exemption, education grant for private schooling, financial support to travel back to your home country every second year and a monthly non-resident allowance.
For detailed information please visit our employee benefits page here.
What else you need to know
International applicants: We recruit internationally and successful candidates are offered visa exemptions. Please take a look at our International Applicants page for further information.
EMBL is a signatory of DORA. Find out how we apply DORA principles to our recruitment and performance assessment processes here.
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.
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.
Closing Date
31/03/2026