Your position

The successful candidate will have the opportunity to leverage cutting-edge long-read single-cell RNA sequencing technologies (PacBio) and apply computational methods to explore full-length transcriptomes from human cancer tissue samples. The project will involve the statistical analysis of large-scale, high-throughput datasets and offers strong opportunities to further develop computational and analytical expertise.

Relevant resources include access to the Basel Ovarian Biobank, state-of-the-art sequencing platforms, high-performance scientific computing (SciCore+), multiplex immunofluorescence, and an ex vivo drug-response platform (DRUGSENS). The project is embedded in a strong collaborative environment, with close interaction with international project partners, partners from the Swiss Tumor Profiler consortium, DBM Bioinformatics and IT, and the DBM Microscopy Facility.

The candidate should have a strong background in computational analysis and a keen interest in long-read sequencing technologies. Excellent teamwork and communication skills in English are required. The PhD candidate will be expected to take an active role in shaping and leading the project, supported by senior lab members and close collaboration partners, within an environment that encourages academic freedom and scientific independence.

In line with the values of the University of Basel (https://www.unibas.ch/en/Research/Values-Ethics/Diversity-and-Inclusion.html), we are committed to fostering an inclusive and diverse working environment that promotes equal opportunities and mutual respect. We are seeking a highly motivated PhD candidate eager to develop innovative strategies to gain novel insights into single-cell transcriptomes within the tumor microenvironment.

Your main tasks will be:
■ Conducting wet-lab (30%) and dry-lab (70%) research activities
■ Performing tissue processing, including quality control, for library preparation and single-cell RNA sequencing
■ Applying and further developing computational tools to analyze and visualize long-read sequencing data
■ Performing and analyzing ex vivo culture experiments to test and validate cell-type–specific isoforms
■ Contributing to the co-supervision of Bachelor's and Master's student projects
■ Willingness to undertake research visits abroad within this collaborative project (computational analysis and spatial proteomics for immune cell subtyping and isoform localization)
■ Supporting the preparation of grant proposals, scientific reports, and peer-reviewed publications



Your profile

■ Highly motivated candidate driven by scientific curiosity and a strong commitment to addressing biologically and clinically relevant research questions.
■ Master's degree in Bioinformatics/Computational Biology, (Bio-)Physics, Biological Sciences, Systems Biology, Molecular Biology, or a related field.
■ Experience in wet-lab work, including cell culture work.
■ Experience in statistical approaches for the analysis of datasets.
■ Strong motivation to work in an international research environment.
■ Strong experience with programming languages (e.g. R or Python).
■ Excellent communication skills and scientific curiosity
■ Plus: Experience with analysis of single-cell technologies

We offer you

■ You will have access to state-of-the-art research equipment in RNA sequencing, cell biology, computational resources, and microscopy.
■ You will work in a dynamic and highly interdisciplinary team including computer scientists, experimentalists and clinicians.
■ You will be involved in cross-disciplinary collaborations and have training opportunities to further develop and grow your scientific interests.
■ Access to a large consortium of experts in different disciplines, such as cancer research, cell biology, computational biology, gynaecological oncology, and immunology.
■ Opportunity to work in close contact with other basic- and clinical-research groups at the Department of Biomedicine and the Biozentrum in Basel.
■ Support from highly competent, experienced team members and potential to lead projects with a high degree of academic freedom.

Key References

Lischetti et al., Ovarian cancer metastasis to the human omentum disrupts organ homeostasis and induces fundamental tissue reprogramming. Nat Commun. 2025 Dec 16;. doi: 10.1038/s41467-025-67557-z. [Epub ahead of print] PubMed PMID: 41397972.

Labrosse et al., Protocol for quantifying drug sensitivity in 3D patient-derived ovarian cancer models. STAR Protoc. 2024 Sep 20;5(3):103274. doi: 10.1016/j.xpro.2024.103274. Epub 2024 Aug 21. PubMed PMID: 39172645; PubMed Central PMCID: PMC11387699.

Kasikova et al., Tertiary lymphoid structures and B cells determine clinically relevant T cell phenotypes in ovarian cancer. Nat Commun. 2024 Mar 21;15(1):2528. doi: 10.1038/s41467-024-46873-w. PubMed PMID: 38514660; PubMed Central PMCID: PMC10957872.

Dondi A et al., Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer. Nat Commun. 2023 Nov 27;14(1):7780. doi: 10.1038/s41467-023-43387-9. PubMed PMID: 38012143; PubMed Central PMCID: PMC10682465.

Irmisch et al., The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell. 2021 Mar 8;39(3):288-293. doi: 10.1016/j.ccell.2021.01.004. Epub 2021 Jan 21. PubMed PMID: 33482122.

Application / Contact

Please send your application to francis.jacob@usb.ch and submit the following documents: A) a cover letter, B) a complete curriculum vitae C) the PhD application form (D) references from at least two referees.