The Data Science Unit at the European Institute of Oncology (IEO) in Milan, Italy, invites applications for a Computational Research Fellowship (Bioinformatician) in the field of AI-driven precision oncology for metastatic colorectal cancer. The position is funded by the Italian Ministry of Health (grant GR-2024-12380102), within the project:
“An artificial intelligence-based multiomic approach to predict the efficacy of EGFR inhibitors in RAS/BRAF wild type metastatic colorectal cancer (ACTOR)”.
The fellow will work at the interface between computational and clinical research, in close collaboration with the Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors at IEO, to develop predictive models of response to anti-EGFR therapy from genomic, transcriptomic, single-cell and microbiome data.
Project Overview
In RAS/BRAF wild type metastatic colorectal cancer, anti-EGFR antibodies combined with chemotherapy are a key treatment option, yet a subset of patients does not benefit. Current selection, based on a handful of genes, misses much of the biology driving response, including tumour heterogeneity, the microenvironment and the gut microbiome.
The project aims to:
- build and externally validate a genomic and transcriptomic model of response
- develop a microbiome-based predictive model
- derive a single-cell-informed prognostic model through synthetic bulk transcriptomic profiles
Responsibilities
The selected fellow will be responsible for:
- Processing, quality control and harmonization of multi-omic data, includingWES, bulk RNA-seq, single-cell RNA-seq and stool microbiome/metagenomic data.
- Designing and implementing machine-learning pipelines for response prediction on high-dimensional, small-sample data: feature selection and stability analysis, nested cross-validation, handling of strong class imbalance (about 10% non-responders), probability calibration, model stacking and model interpretation (e.g. SHAP).
- Single-cell analyses and the generation of synthetic bulk profiles to bridge single-cell and bulk cohorts.
- Supporting the analysis of genomic alterations emerging at progression and of microbiome shifts before and after treatment.
- Contributing to scientific publications, conference presentations and project dissemination.
Candidate Profile
We are seeking a highly motivated, independent and collaborative researcher with a MSc (with experience) or PhD in bioinformatics, computational biology, computer science, statistics, physics, biomedical engineering or a related field.
Candidates must bring solid experience in at least one of the following two areas, and the willingness to grow in the other:
A. Machine learning
- Machine learning
- Hands-on experience building and validating supervised models (e.g. penalized regression, random forests, gradient boosting, neural networks), ideally on biological or clinical data.
- Sound understanding of validation design and overfitting control in small-n, high-dimensional settings (nested cross-validation, external validation, data leakage).
- Familiarity with imbalanced classification, calibration, feature selection and model interpretability.
B. Omics data analysis
- Experience in the analysis of sequencing data, in particular single-cell RNA-seq and/or bulk RNA-seq; experience with DNA-seq/variant data or microbiome data is a plus.
- Knowledge of sequencing quality metrics, batch effects, confounders and bias control strategies.
- Familiarity with best practices for data integration across samples, cohorts and modalities.
Required for all candidates
- Proficiency in Python and/or R (e.g. scikit-learn, XGBoost, PyTorch/TensorFlow; Scanpy/Seurat, Bioconductor).
- Good practices in reproducible research: version control (git), working on Linux/HPC environments; workflow managers (Nextflow) and containers are a plus.
- Strong communication skills, fluency in English, and the ability to work in an interdisciplinary and international environment alongside clinicians, biologists and statisticians.
Will be considered a plus
- Experience with clinical trial or real-world clinical data, survival analysis, or liquid biopsy/ctDNA data.
- Experience with cell-type deconvolution, pseudo-bulk or other methods linking single-cell and bulk transcriptomics.
- A publication record in computational biology or oncology.
What We Offer
- The opportunity to work on unique, well-annotated clinical trial datasets with direct translational impact on patient selection.
- A dynamic Data Science Unit within a leading comprehensive cancer centre, with close interaction with clinical and experimental groups.
- Training opportunities and active involvement in publications and international collaborations.
Application procedure
1) Apply directly through the IEO website;
2) Additionally, please email your CV, a short cover letter describing your experience in machine learning and/or omics data analysis, and the contacts of two referees to:
Contract information
Position: Bioinformatician Fellow
Funding: Italian Ministry of Health, GR-2024-12380102 (ACTOR project)
Starting date: 01/01/2027
Duration: 1 year (extendable up to 3 years)
Salary: 25-32k depending on experience
Posted on 30th October 2026