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PhD Student
PhD: Single-cell RNA for Chromosomal Instability in Cancer (computational) Full-time
The project
Most cancers carry abnormal chromosome numbers (aneuploidy), but malignant cells sometimes exhibit chromosomal instability (CIN): the ongoing, cell-to-cell reshuffling of chromosomes that continuously generates new tumor variants. These variants can survive therapy, evade immunity, and drive relapse. Despite the importance of CIN, measuring it at scale in patient tumors remains difficult, especially for rare, cell-specific events that serve as evidence of ongoing CIN.
This PhD will work at the leading edge of single-cell transcriptomics and modern statistical/AI methodology to address this gap using in-house developed cell atlases. We will develop and benchmark approaches that infer copy-number changes and CIN dynamics directly from single-cell RNA-seq, then connect these CIN signals to (i) the molecular programs that allow tumor cells to tolerate genome chaos and (ii) actionable vulnerabilities that could improve patient stratification and combination therapy choices.
You’ll be part of a tightly integrated team that uses computational models generate hypotheses and, with the help of partner labs, validate them in controlled systems. The end goal is a mechanistic and clinically relevant map of how CIN shapes cancer behavior and where it can be targeted.
What you will do
- Build robust, reproducible scRNA-seq analysis workflows and benchmark methods (simulation + real datasets)
- Develop rare-event–sensitive CIN/aneuploidy metrics and validate them across multiple cancer cohorts
- Link CIN programs to outcomes and therapy response using large public datasets and modern predictive modeling
- Integrate CIN signatures with functional dependency resources to shortlist candidate vulnerabilities for validation
- Contribute to open-source releases and publications
Who we’re looking for
Required
- MSc (or equivalent) in molecular biology, bioinformatics, computational biology, statistics, computer science, or similar
- Solid programming in R and/or Python, and/or experience analyzing sequencing data in a bioinformatics context
Preferred
- Prior work with single-cell RNA-seq (QC, normalization/integration, differential expression, clustering)
- Comfort with statistics/ML (statistical tests, regression models, regularization)
- General understanding of cancer biology (tumor evolution, aneuploidy/CNAs, resistance development)
What we offer
- A fully funded position according to the collective labor agreement for Austrian PhD students (3 years, 30 hours/week), with English as the working language
- Training at a strong medical research university (MUI) with access to HPC, expert bioinformatics mentorship, and close experimental collaborations
- A project with clear scientific novelty, real translational relevance, and multiple publishable components
- Innsbruck: an exceptional quality-of-life city: mountains, trails, climbing, biking, and skiing just outside your door
How to apply
Please send one PDF including 1) CV including names/contact details of two referees, 2) a short motivation letter (1 page) explaining why your background is a good fit for the project, 3) optional: link to code/GitHub or an analysis to the linked email address quoting MEDI-20369. Deadline: Feb 11th.
Questions about the position can be addressed to m.schubert at i-med dot ac dot at.