• Reference: AIPRECCGSO
• Start date: 26/08/2025 0:00:00
• Expiration Date: 04/09/2025 23:59:00
  
  

The Computational Cancer Genomics Lab adopts an integrated methodology to delve into the disrupted protein-protein interaction network. Our primary focus involves analyzing extensive cancer genomics data and building models to comprehend condition-specific interaction dynamics. Stemming from our model study, our paramount objective is to gain a complete understanding of the context-specific cancer fitness landscape by examining position-specific interaction networks.

Despite the growing volume of cancer genomics data, most existing network models oversimplify interaction dynamics by treating them as static and context-independent. However, tumorigenesis is a highly dynamic process influenced by cell type, mutational context, and microenvironment. Our approach addresses this gap by focusing on position-specific and condition-specific PPIs, offering a more nuanced and accurate understanding of the molecular underpinnings of cancer. This is critical for identifying precise therapeutic targets and biomarkers. Furthermore, collaboration with global institutions ensures access to diverse datasets and methodologies, increasing the robustness and generalizability of our models. The requested support is therefore essential to enable computational resource development, cross-institutional data integration, and high-resolution network modeling required to advance this innovative research.

Roles And Responsibilities Of Proposed Position

• Constructing the pipeline for cancer genomics data collection and variant classification.
• Analysing large-scale protein-protein interaction screening result.
• Developing computational model to understand perturbed interaction network.
  
  

Selection Criteria

• Master’s degree or equivalent experience in Bioinformatics, Computational biology, Statistics or relevant field.
• Experience with big-data/genomics is desirable.
• Technical skills:
• Programming in different languages such as R, Python or others.
• Familiarity with super computing system and workflow management systems.
• Experience in large-scale sequencing data analysis.
• Ability to work in a team, excellent attention to detail and the desire to learn and improve.
• Excellent communication skills in English.
  
  

The CNIO Offers

• The opportunity to be part of one of the few European Cancer Research Centres of excellence.
• An excellent working, multidisciplinary environment.
• Competitive salary.
• 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).