- 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 analysing 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 modelling: 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 modelling 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 modelling 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 analysing 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. Adaptable 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).

#J-18808-Ljbffr

📌 Predoctoral research assistant for the computational cancer genomics group (Comunidad de Madrid)
🏢 CNIO - Spanish National Cancer Research Centre
📍 Comunidad de Madrid