PhD Position: AI for Spatiotemporal Analysis of Cell Behavior in Large-Scale Bioimage Data

About the Project

PhD Position: AI for Spatiotemporal Analysis of Cell Behavior in Large-Scale Bioimage Data

Faculty of Informatics, Masaryk University (FI MU), Brno, Czech Republic

Supervisor: Martin Maška

Host centre: Centre for Biomedical Image Analysis (CBIA), FI MU

Study programme: Ph.D. in Informatics

Form: Full-time (preferred)

Start: Spring 2027

Application deadline: November 30, 2026

Research Topic

We invite applications for a PhD position at the intersection of artificial intelligence, image segmentation, and object tracking, with primary applications in bioimage analysis.

The PhD project will be conducted within the Centre for Biomedical Image Analysis (CBIA) at the Faculty of Informatics, Masaryk University. CBIA provides a multidisciplinary research environment combining computer science, biomedical imaging, and clinical collaboration.

Modern fluorescence microscopy facilitates time-lapse observations of cells at unprecedented spatiotemporal resolutions, calling for reliable and automated bioimage analysis pipelines to quantitatively analyze the captured bioimage data in a reproducible fashion instead of conducting subjective and arduous manual analysis by experienced humans. Such pipelines are of immense need in developmental studies that routinely generate terabytes of multidimensional and multichannel image data with hundreds or thousands of collectively evolving cells. The large-scale nature of such bioimage data heavily limits the feasibility of manual annotations, which in turn is reflected in the limited reliability and depth of drawn biological conclusions about developing cell tissues. The primary objective of the PhD project is to develop deep-learning-based pipelines for reliable and resource-efficient segmentation and tracking of collectively evolving cells with nuclear, cytoskeletal, or membrane labeling during embryogenesis and organogenesis.

Possible Research Directions

• Cell segmentation and tracking using foundation models
• Cell tracking using graph neural networks
• Semi-supervised / zero-shot cell instance segmentation
• Shape- and motion-aware cell embeddings
• Carbon-footprint-efficient tracking of cells

The exact topic will be specified jointly with the supervisor, depending on the candidate’s experience and interests.

Programme & Study Environment

The position is embedded in the four-year doctoral programme in Informatics at FI MU, delivered in English or Czech.

Doctoral students are expected to:

• Conduct independent research under supervision
• Publish at international venues
• Participate in conferences and research stays abroad

Funding & Employment Conditions (full-time students)

• Tuition fee: 0 CZK (also for English-language doctoral study)
• Basic doctoral scholarship: 18,000 CZK / month
• Employment contract (20% FTE): ~7,000 CZK net / month
• Publication-performance scholarship: up to 7,000 CZK / month

Active PhD students typically reach ~32,000 CZK net monthly income during the standard study period.

Candidate Profile

Required

• MSc in Computer Science, Biomedical Engineering, or related field
• Strong background in deep learning and image processing
• Programming skills (Python, PyTorch, scikit-image/OpenCV)
• Solid mathematical foundation

Preferred

• Experience with image segmentation or object tracking
• Biomedical imaging background
• Publication or open-source track record
• GPU / HPC experience

Application Procedure

Apply through the Masaryk University e-application system https://www.fi.muni.cz/admission/doctoral/application.html.en

Contact

For informal inquiries about the research topic:

Martin Maška (Faculty of Informatics, Masaryk University)

Email: xmaska@fi.muni.cz

Web: https://www.muni.cz/en/people/60734