Postdoctoral Position Computational Biology Endocytosis, Signalling and Cancer Lab

Postdoctoral Position Computational Biology Endocytosis, Signalling and Cancer Lab

IEO - European Institute of Oncology

Milan, Italy

We are seeking a Postdoctoral Researcher in Computational Biology to join the Endocytosis, Signalling and Cancer Laboratory headed by Prof. Pier Paolo Di Fiore at the European Institute of Oncology (IEO) in Milan. The position is part of a project funded by the Italian Fund for Science (FIS) and aims to improve prognostic stratification and treatment of breast cancer patients by exploiting phenotypic intertumoral heterogeneity through an integrated clinical, multi-omics, and biological platform.

About the Project

Breast cancer management is hindered by extensive molecular and phenotypic heterogeneity. Despite advances in molecular stratification and precision medicine, major unmet clinical needs persist, particularly in intermediate-risk Luminal breast cancer and triple-negative breast cancer (TNBC), the most aggressive subtype.

Our laboratory has recently developed two multigene signatures: PetSign, capturing metabolic (glycolytic) alterations, and StemPrintER, reflecting the acquisition of cancer stem cell properties. This project aims to leverage these signatures to improve prognostic stratification of Luminal and TNBC patients and to identify FDA-approved drugs capable of targeting these phenotypes.

Role and Responsibilities

The successful candidate will:

• Lead the development and optimization of prognostic risk score algorithms
• Benchmark novel signatures against currently used prognostic and predictive multigene tests
• Integrate clinical and multi-omics datasets to improve prognostic performance
• Correlate treatment response with risk scores to identify candidate therapeutic strategies targeting specific phenotypes

Qualifications

We seek creative, self-driven scientists with:

• A PhD in Computational Biology, Bioinformatics, Biostatistics, Computer Science, Systems Biology, or a related quantitative discipline
• Strong expertise in computational modelling and statistical analysis of biological data
• Experience in developing prognostic models and/or risk score algorithms, particularly in oncology, is highly desirable
• Familiarity with survival analysis, multivariate modelling, and validation of predictive signatures is a plus
• Proficiency in MATLAB, Python and/or R
• A genuine interest in cancer biology and translational research