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

About the project

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

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