Computational Scientist and Bioinformatician

IFOM is an internationally recognized non-profit cancer research institute supported by the Italian Association for Cancer Research (AIRC), based in Milan within a highly dynamic urban environment.

IFOM’s continued success is ensured by the recruitment of highly selected undergraduate, graduate and post-graduate researchers from over 24 countries worldwide.

IFOM is hosted within a research campus shared with the European Institute of Oncology and the Italian Institute of Technology.

IFOM is committed to creating a safe and inclusive environment, based on mutual respect and the enhancement of diversity, offering equal job opportunities to all qualified candidates.

We seek for our Institute: Computational Scientist and Bioinformatician | Liquid Biopsy and MRD

The successful candidate will join the Computational Oncology and Bioinformatics Unit and report directly to the Head of the Unit. This is a permanent, full-time research position focused on liquid biopsy, cancer genomics and molecular residual disease (MRD).

The research will address biological and translational questions in tumour genetics, molecular monitoring, tumour evolution and treatment response. Particular attention will be given to the interpretation of circulating tumour DNA (ctDNA) in plasma, including the challenges of detecting low-abundance tumour-derived genetic alterations.

The Unit operates as a research and development structure, not as a task-based facility. Computational approaches are developed and applied within scientific projects to answer clearly defined biological and clinical questions, rather than as stand-alone technical services.

The Unit aims to generate biologically meaningful and clinically relevant knowledge by:

• investigating the genetic features and dynamics of circulating tumour DNA in cancer;
• characterising molecular residual disease, tumour heterogeneity, clonal evolution and treatment response through longitudinal liquid-biopsy studies;
• developing and critically evaluating computational methodologies for identifying and interpreting somatic genetic alterations in plasma and matched tumour tissue;
• integrating genomic observations with clinical information to investigate biomarkers relevant to disease monitoring, recurrence and therapeutic response.

We are looking for a scientist holding a PhD, with at least 3 years of relevant research experience in cancer genomics, computational biology, bioinformatics or a related field (including doctoral research). Previous experience in liquid biopsy or MRD is desirable, but strong expertise in cancer genetics and computational methodology is equally valued.

ABOUT THE POSITION - KEY RESPONSIBILITIES

• Exploring original scientific hypotheses in liquid biopsy and MRD, including limitations of existing approaches and opportunities for methodological innovation.
• Critically evaluating genomic results, including the biological and analytical factors affecting sensitivity, specificity and interpretation of low-abundance tumour-derived signals in liquid biopsy and MRD.
• Designing robust and reproducible analytical and validation strategies, using computational tools and institutional infrastructure as instruments of scientific research.
• Handling research and clinical data responsibly, in accordance with approved research protocols, institutional policies, applicable privacy requirements and research ethics.
• Contributing to scientific manuscripts and sharing the information necessary for productive research collaborations.
• Developing independent methodological contributions and helping advance the scientific programme and research publications of the Unit.
• Developing and evaluating novel computational approaches to investigate cancer genetics, improve the detection of low-abundance tumour-derived signals and address unresolved biological questions in liquid biopsy and MRD.

Qualification

• Strong foundations in cancer genetics, molecular oncology, genomics and/or computational biology;
• Solid knowledge of statistics, quantitative methods and sound principles of experimental and observational data analysis;
• Demonstrated experience in the analysis of NGS and genomic data and in the biological interpretation of complex datasets;
• Demonstrated prog