We have several openings for Master’s or internship students!
Projects can be purely computational, for example to explore high-dimensional omic data sets from both single cell or bulk samples to study (mesenchymal) stem cell differentiation trajectories and the underlying regulatory networks or to investigate the molecular mechanisms underlying regulatory and phenotypic variation. In addition, we have several technology-centric projects incorporating in-house developed high-throughput or microfluidic tools to study transcription factor behavior or to perform cellular phenotyping, coupling high-resolution imaging to transcriptomic read-outs of single cells.
If interested, please contact [email protected]
Current opportunity: Integrated imaging and scRNA-seq for prediction and enrichment of high performant cells
Contact: Jörn Pezoldt [email protected]
Modern life sciences increasingly require technologies that bridge fundamental single-cell biology with real-world biomedical applications. In biologics development, current workflows lack scalable tools to link single-cell phenotype to functional performance. We developed a deterministic microfluidic platform to bridge that gap. IRIS (Ref 1) platform was developed to advance single-cell phenomics by integrating deterministic microfluidics, high-throughput live-cell imaging, and AI-driven analysis into a unified system. IRIS enables quantitative, label-free characterization and real-time recovery of living cells based on complex phenotypic signatures. This Master’s project will apply the IRIS platform to biologics-producing cells to identify image-derived phenotypes linked to productivity, antibody-quality, and cellular state. By combining high-content organelle imaging with whole-genome transcriptomics, the project will generate AI-native single-cell datasets connecting cellular morphology to molecular programs and performance (Ref 2). Based on the phenomic AI-native data we train machine-learning classifiers for look-once, label-free sorting of high-producer cells. Sorted single cells will then be evaluated in nanoliter-scale culture systems to assess productivity and stability, establishing image-based phenomics as a tool for both single-cell research and advanced biomanufacturing.
IRIS | Advancing single-cell science and biopharma
- Deterministic single-cell handling enabling reproducible phenomic profiling
- AI-native datasets linking morphology to function
- Integrated profiling and functional cell recovery in one platform
- Bridging fundamental life science research with biomanufacturing applications
Skills & profile
- Strong foundation in molecular and cell biology
- Technology-affinity for integrated microfluidic systems
- Curiosity for image analysis and transcriptomics
- Motivation to work at the interface of cell & molecular biology, engineering, and AI
- Working knowledge of Python and Bash for data processing and computational workflows
The project offers
- Training on a first-of-its-kind high-throughput deterministic microfluidic system
- Wet-lab: Miniaturized nanoliter cell culture, IRIS-derived scRNA-seq workflow (Ref 1)
- Dry-lab: Single cell phenomic data analysis (Ref 1, Ref 2)
- Work in a focused, solution-driven interdisciplinary team
- Contribution to an ambitious platform shaping the future of cell phenomic processing
References
- IRIS technology development and research and immunology use-cases | Single-cell phenomics through integrated imaging and molecular profiling
https://www.biorxiv.org/content/10.1101/2025.11.28.690954v1 - COSMIC predictive connection between morphology and gene expression | Generative modeling reveals the connection between cellular morphology and gene expression
https://www.biorxiv.org/content/10.64898/2026.01.22.700673v1