Predoctoral Research Assistant for the Computational Cancer Genomics Group

CNIO - Spanish National Cancer Research Centre; Madrid; On-site; EUR 19,000 - 26,000; Full time

Benefits offered by this job

Health insurance

Meals

Transportation

Childcare

Job summary

CNIO - Spanish National Cancer Research Centre in Madrid seeks a researcher to conduct genomic data analyses and develop statistical and AI models on cancer datasets. You will work with big data from patient cohorts, apply advanced analytics, and build scalable pipelines for high-throughput sequencing data.

English communication is essential. The role emphasizes handling diverse omics data, collaboration across international teams, and contributing to cancer biology insights while using R/Python

Qualifications

• 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.

Responsibilities

• 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.

Skills

R

Python

Big data processing

Education

Master’s degree or equivalent in quantitative field

Job description

• 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. Flexible 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).