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 the lead software developer on 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. Working alongside a first developer focused on algorithmic development and method optimisation, your role will centre on the technical software infrastructure of the platform: designing and maintaining the web-based framework and APIs, building deployment and CI/CD pipelines, curating and integrating the diverse prior knowledge networks that underpin all contextualisation methods, and developing the interface between NetworkCommons and the Open Targets Platform.
Prior knowledge networks—including manually curated signalling interactions, protein-protein interaction databases, transcriptional regulatory networks, and functional association resources—are the foundation on which all network contextualisation methods operate. The quality, coverage, and accessibility of these resources directly determines the quality of the biological hypotheses produced. Your expertise in building and maintaining large-scale biological knowledge databases will be essential for ensuring that NetworkCommons provides researchers with the best available prior knowledge for their specific biological questions, properly harmonised and programmatically accessible.
You will also lead the development of extended platform features funded under Tier 2 of the project, including the integration of gene regulatory network (GRN) inference methods, pathway and functional enrichment analysis tools, interactive network visualisation, and robust user-friendly APIs with standardised inputs and outputs. You will build and maintain the deployment infrastructure (Docker, CI/CD, reproducible workflow pipelines) for use by consortium partners. You will support the Tier 2 benchmarking case studies—covering additional perturbation datasets (Tahoe-100M, immune cell cytokine stimulation), transcriptome-to-signalling inference from patient cohorts (TCGA/CPTAC), and patient stratification across oncology, IBD, and neuroinflammation—by ensuring the platform handles the required data types and knowledge resources.
Key responsibilities:
Curate, harmonise, and manage prior knowledge networks from diverse sources, including curated causal signalling interactions, protein-protein interaction databases (e.g., OmniPath, STRING, IntAct), transcriptional regulatory networks, miRNA regulatory interactions, co-expression networks, and functional association resources, ensuring consistent annotation, quality control, and programmatic accessibility within the platform.
Design and implement the knowledge resource layer of NetworkCommons, providing flexible APIs for selecting, combining, and filtering prior knowledge networks based on interaction type (causal, physical, functional), curation level, tissue or cell-type specificity, and licensing requirements (enabling modular exclusion of resources restricted for commercial use).
Design, develop, and maintain the NetworkCommons Python library and web-based framework, including package architecture, RESTful APIs (using Flask, FastAPI, or equivalent), testing infrastructure, documentation, and release management, in coordination with the first developer who focuses on the algorithmic core.
Integrate gene regulatory network (GRN) inference methods that have been benchmarked by the Saez-Rodriguez group into the platform, extending NetworkCommons beyond signalling network contextualisation.
Implement additional analytical features including pathway and functional enrichment analysis (with cell-type-specific analysis for single-cell inputs), interactive network visualisation with Cytoscape web app and NDEx integration for network storage and reuse, and maxflow/shortest path query tools.
Develop the interface between NetworkCommons and the Open Targets Platform, working with Open Targets platform developers to enable users to select methods, run analyses, and visualise context-specific networks interactively within the platform.
Support Tier 2 benchmarking case studies by ensuring appropriate data ingestion and prior knowledge network provision for: large-scale single-cell perturbation datasets (Tahoe-100M, cytokine stimulation atlas), transcriptome-to-signalling inference using TCGA/CPTAC data, and patient stratification across oncology (TCGA), IBD (THAMES, OpenIBD, UK IBD Bioresource), and neuroinflammation (Alzheimer, ALS, Parkinson) cohorts.
Engage with academic and industry partners (GSK, Genentech, MSD, Sanger Institute, Imperial College London) through regular project meetings to gather feedback on knowledge resource needs, platform usability, and translational applicability.
Build and maintain the deployment and distribution infrastructure of the platform, including Docker images for local installation by consortium partners, CI/CD pipelines, and reproducible benchmarking workflows (Nextflow/Snakemake), ensuring that resources and tools requiring commercial licences can be modularly excluded.
Qualifications and Experience
Essential:
A PhD (or equivalent experience) in bioinformatics, computational biology, molecular biology, or a related discipline, with a strong computational component.
Extensive experience in curating, integrating, and managing biological knowledge resources, particularly molecular interaction databases (protein-protein interactions, signalling pathways, regulatory networks).
Strong software development skills in Python, including experience with package development, API design, testing, and documentation.
Deep familiarity with biological prior knowledge resources and their data models (e.g., OmniPath, Reactome, STRING, IntAct, KEGG, or equivalent databases).
Experience with network biology approaches and graph data structures (e.g., NetworkX, igraph).
Experience building web applications or RESTful APIs (e.g., using Flask, FastAPI, or Django).
Proficiency with version control (Git), CI/CD systems, and containerisation (Docker).
Ability to work collaboratively in interdisciplinary, international teams spanning computational scientists, biologists, and pharmaceutical researchers.
Desirable:
Experience building and maintaining large-scale biological databases or knowledge graphs, including data harmonisation across heterogeneous sources and ontology mapping.
Familiarity with signalling pathway biology, including transcriptional regulation, post-translational modifications, and miRNA-mediated regulation.
Experience with R and the Bioconductor ecosystem, and interoperability between Python and R.
Experience with workflow management systems (Nextflow, Snakemake) for reproducible computational pipelines.
Familiarity with omics data types (transcriptomics, proteomics, phosphoproteomics) and their analysis, including single-cell workflows.
Experience with Linux system administration and deployment of scientific web services.
Experience with network visualisation tools (Cytoscape, NDEx) and their programmatic APIs.
Track record of contributing to widely used open-source scientific software projects.
Salary: Grade 6 - monthly salary from £3,695.61 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