• Reference: AIPRECCGSO3
• Start date: 27/10/2026 0:00:00
• Expiration Date: 05/11/2026 23:59:00 The Computational Cancer Genomics Lab adopts an integrated methodology to unravel disrupted protein-protein interaction networks in cancer. By analyzing extensive cancer genomics and multi-omics datasets, we build statistical and computational models to comprehend condition-specific interaction dynamics. Our primary objective is to map the context-specific cancer fitness landscape by examining position-specific interaction networks, supported by joint international collaborations. Key Focus Areas Genomics & multi-omics analysis: Process and integrate large-scale genomic and clinical cohort data from cancer patients. Computational & AI modeling: Develop statistical models and machine learning approaches to investigate condition-specific driver gene mechanisms and fitness landscapes. Interaction networks & emerging themes: Explore position-specific interaction networks, incorporating proteomics-based co-abundance network analyses and investigating subtle cross-talks in aging-cancer bifurcation dynamics. Methodological expansion & multi-omics integration: Unraveling complex position-specific interaction networks and mapping cancer fitness landscapes requires dedicated expertise capable of bridging genomic big data with emerging proteomics-based co-abundance network analyses. Complex biological frontiers: Investigating sophisticated biological transitions—such as the interplay between aging-cancer bifurcation dynamics and mutational contexts—demands tailored statistical modeling and AI/machine learning capabilities. Roles And Responsibilities Of Proposed Position
• Conduct research and exploratory data analysis using genomic big data derived from cancer patients and clinical cohorts.
• Develop and apply statistical models and machine learning/AI techniques to decipher complex biological mechanisms and driver gene interactions from big data.
• Implement and adapt scalable computational pipelines to streamline the analysis of high-throughput sequencing datasets. Selection Criteria
• Master’s degree (or equivalent experience) in Bioinformatics, Computational Biology, Statistics, Computer Science, Genomics, or a related quantitative field.
• Familiarity or hands-on experience with handling genomic big data, particularly from cancer patients or clinical/genomic cohorts (e.g., WGS, WES, RNA-seq, or single-cell data).
• Interest or basic background in applying statistical or computational modeling to interpret biological systems and disease mechanisms.
• Proficiency in programming languages commonly used in bioinformatics, such as R, Python, or equivalent languages.
• Hands-on experience in processing and analyzing big data.
• Excellent written and verbal communication skills in English are required. All application materials, including a detailed CV, must be submitted in English. The CNIO Offers
• Joining a research centre of international relevance.
• Gross annual salary between €19,475 and €25,615.
• Social benefits. Versátil compensation (Health insurance, meals, transportation, childcare).
• Estos contratos están vinculados a la acreditación del CNIO como Centro de Excelencia Severo Ochoa CEX2024-001442-S, financiado por el Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (MICIU/AEI/10.13039/501100011033).

📌 Predoctoral Research Assistant for the Computational Cancer Genomics Group (Madrid)
🏢 Spanish National Cancer Research Centre (CNIO)
📍 Madrid