Software Developer - Interoperability for Single-Cell and Spatial Omics Methods

German Cancer Research Center in the Helmholtz Association (DKFZ); Deutschland; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading cancer research center in Germany is seeking a Software Developer to enhance interoperability for single-cell and spatial omics methods. You will work with Python and R ecosystems, contributing to open-source tools that foster collaborative scientific research. This role involves developing conversion tools, automating infrastructure, and improving documentation, with opportunities for personal development and networking in a family-friendly environment. Join us in advancing cancer research!

Leistungen

30 days of vacation per year

Flexible working hours

Remuneration according to TV-L including pension plan

Possibility of mobile work

Qualifikationen

• M.Sc. or equivalent in computer science, statistics, mathematics, physics, engineering, or a biological science with demonstrated experience in statistics and software development.
• Software development skills in Python and familiarity with the data science and geospatial Python stack.
• Experience with open-source development practices: Git/GitHub, code review, testing, packaging.

Aufgaben

• Expand interoperability between scverse and R/Bioconductor ecosystems.
• Build and automate infrastructure for continued interoperability.
• Improve documentation with clear tutorials and examples.

Kenntnisse

Software development skills in Python

Familiarity with data science and geospatial Python stack

Excellent collaboration and communication skills

Open source development practices

Ausbildung

M.Sc. or equivalent in relevant fields

Jobbeschreibung

Software Developer - Interoperability for Single-Cell and Spatial Omics Methods

Reference number: 2025-0331

The research group of Prof. Dr. Oliver Stegle is seeking a software developer to advance interoperability for single-cell and spatial omics across the scverse (Python) and Bioconductor (R) ecosystems. The project focuses on enhancing the FAIR capabilities of SpatialData, a modern Python framework for spatial omics, across platforms, and on strengthening its language-agnostic file format based on the OME-NGFF open standard. You will help ensure seamless cross-language usage, robust data exchange, and clear specifications that serve both communities.

You will be part of a vibrant open‑source environment at the interface of software development and biomedical research—specifically within the global scverse community. You will have the opportunity to shape widely used, FAIR‑by‑design tools that accelerate single‑cell and spatial omics research across both Python and R ecosystems. The Stegle group is jointly based at DKFZ and EMBL and embedded in Heidelberg’s vibrant ecosystem for data science, machine learning, and computational biology. We collaborate closely with research groups across Germany and worldwide, and we actively engage with open‑source communities through coordinated development, cross‑consortium events, and hackathons.

Your Tasks

• Expand the bridge between the scverse and R/Bioconductor ecosystems through interoperable software development focused on the OME‑Zarr file format and the SpatialData framework.
• Build and automate an infrastructure to ensure continued interoperability of SpatialData across R/Python, including robust converters, I/O layers, and validation tests.
• Improve the documentation of the SpatialData format with clear tutorials, examples, and contributor guides that lower the barrier to entry.
• Contribute to the NGFF specification and help support the incoming versions, in particular NGFF 0.6/RFC‑5 and RFC‑8, via updates to the SpatialData codebase, but also by contributing to the NGFF codebase.
• Engage with the open‑source scverse and NGFF communities: triage issues; review PRs; collaborate in public forums and with the possibility to participate in cross‑consortium events, sprints, and hackathons.

Your Profile

Required Qualifications:

• An M.Sc. or equivalent in computer science, statistics, mathematics, physics, engineering, or a biological science with demonstrated experience in statistics and software development.
• Software development skills in Python and familiarity with the data science and geospatial Python stack (e.g., xarray, dask; GeoPandas/Shapely a plus).
• Open‑source development practices: Git/GitHub, code review, testing (e.g., pytest), packaging, CI/CD (e.g., GitHub Actions), and documentation (e.g., Sphinx).
• Excellent collaboration and communication skills; motivation to contribute to a scientific, community‑driven project as a supportive, creative, and responsible team member.

Preferred Qualifications:

• Experience with R/Bioconductor, AnnData/SummarizedExperiment, Zarr/NGFF, or designing cross‑language APIs and data schemas.
• Research experience implementing statistical learning or machine learning (e.g., Bayesian inference, deep learning), ideally connected to spatial omics, and experience with frameworks like PyTorch, Keras, Pyro, or TensorFlow.

Application process

• Interested candidates should submit a cover letter, a CV, and the contact details of 2 referees via our online application tool. Please elaborate in your cover letter on your (open‑source) software development experience, or willingness to contribute. Ideally, please provide links to Git repositories where you made significant contributions.
• We will conduct two rounds of interviews. In the first round, taking place online, you will be asked to bring and comment on a code sample/project of your choice that you previously wrote, for approximately 15‑30 minutes. You can also choose to bring a newly written short code sample (100‑500 lines of code). The rest of the interview will follow with general and technical questions related to the job description.
• In the second round, we will ask you to perform a spoken code review on a short sample code that we will prepare, and you will also meet the team (either online or in‑person).
• You are encouraged to ask questions, whether technical or related to team culture, at any stage of the interview process.
• Applications will be reviewed on a rolling basis, so you are encouraged to apply before the deadline.

Recent relevant publications

• Marconato, L., Palla, G., Yamauchi, K.A. et al. SpatialData: an open and universal data framework for spatial omics. Nat Methods 22, 58–62 (2025). https://doi.org/10.1038/s41592-024-02212-x
• Crowell, H. L., Dong Y., Billato I. et al. Orchestrating Spatial Transcriptomics Analysis with Bioconductor. bioRxiv 2025.11.20.688607; doi: https://doi.org/10.1101/2025.11.20.688607
• Moore, J., Basurto-Lozada, D., Besson, S. et al. OME‑Zarr: a cloud‑optimized bioimaging file format with international community support. Histochem Cell Biol 160, 223–251 (2023). https://doi.org/10.1007/s00418-023-02209-1
• Virshup, I., Bredikhin, D., Heumos, L. et al. The scverse project provides a computational ecosystem for single‑cell omics data analysis. Nat Biotechnol 41, 604–606 (2023). https://doi.org/10.1038/s41587-023-01733-8

We Offer

• Excellent framework conditions: state‑of‑the‑art equipment and opportunities for international networking at the highest level.
• 30 days of vacation per year.
• Flexible working hours.
• Remuneration according to TV‑L incl. occupational pension plan and capital‑forming payments.
• Possibility of mobile work and part‑time work.
• Family‑friendly working environment.
• Sustainable travel to work: subsidized Germany job ticket.
• Unleash your full potential: targeted offers for your personal development promote your talents.
• Our Corporate Health Management Program offers a holistic approach to your well‑being.

Contact

Prof. Dr. Oliver Stegle
Phone: [phone]

The position is limited until 31.12.2026.

Application Deadline: 13.01.2026

Applications by e‑mail cannot be accepted.

Are you interested?

Then become part of the DKFZ and join us in contributing to a life without cancer!

Notice

We are subject to the regulations of the Infection Protection Act (IfSG). Therefore, all our employees must provide proof of immunity against measles.

We are convinced that an innovative research and working environment thrives on the diversity of its employees. Therefore, we welcome applications from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference if they have the same aptitude.