Special PhD positions at the Department of Visual Computing

2020

Department of Visual Computing, Faculty of Informatics, Masaryk University, announces an open call for one PhD position starting from the Autumn 2020 term, with applications in the following areas:

The deadline for application is May 12, 2020.

General information

The student is expected to have (or be about to finish) a Master level education in Computer Science, Electrical Engineering, Biomedical Engineering, or related areas, with demonstrated overview in the field. Good knowledge of English language is expected as well as willingness to spend 3-6 months in a collaborating group abroad during the PhD studies; prior knowledge of Czech is not necessary.

Stipend

The applications will be evaluated by the department committee, whose members will choose the best applicant. The announced PhD position is funded with an extra department stipend of 10 000 CZK per month (summing to at least 28 700 CZK with the standard faculty stipend). The stipend is granted to the successful applicant for the first 2 years, with an expected renewal (after an evaluation) for another 2 years. The total length of study is 4 years.

Application procedure

Applicants are advised to contact directly their perspective supervisor (as listed below) for more specific details, well ahead of the deadline. The final applications consisting of CV (including education, degrees and dates, publications/scientific presentations, skills/experiences in programming languages, project work, academic awards, etc.), motivation letter explaining why you apply specifically for this project and why you are the perfect candidate, transcript of the grades from the Master’s and Bachelor's degree, two references (written or just two contact names), and possibly other relevant documents supporting the candidate's excellence should be sent to the Head of the Department

  • Assoc.Prof. Petr Matula, before the respective deadline for application (see above)

The candidates are still obliged to pass the standard admission procedure for doctoral study. The stipend can be awarded only after successfully completing the standard admission procedure.


Topic: Cell Behaviour Studies using Machine Learning

Supervisor: prof. RNDr. Michal Kozubek, Ph.D.
Area: Biomedical image processing

Understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease, hence there are ongoing efforts to integrate all the vast and diverse information about cells to create a credible model of cell morphology and behavior [1]. While cell morphology has been studied rather thoroughly for decades, there is still lack of information on cell behavior under various conditions. Therefore, the goal of the PhD student will be to contribute to the learning and understanding of cell behavior, especially by applying machine learning methods to the analysis of a large collection of videos from optical microscopes. Both publicly available datasets and new videos of cells will be analyzed. Know-how on cell tracking will be provided so that the student will be able to concentrate on the behavior analysis. At the beginning of the work it will be necessary to define a suitable collection of behavior descriptors (temporal features) – there are not many of them in contrast to morphology descriptors (spatial features). Afterwards, the cell behavior will be studied using these descriptors. And because cells are sociable entities, it will be necessary to study not only the behavior of single cells but also groups of cells and interactions between cells. Eventually, the computer should be able to predict subsequent behavior of cells (key temporal features) based on their previous behavior, i.e. predict “the rest of the story” for an unfinished video (e.g., that the cells will die or form some structure). This would have an immense impact, e.g. for quality control in stem cell research (to check that stem cells behave in a correct way).

[1] Ortiz-de-Solórzano C, Muñoz-Barrutia A, Meijering E, Kozubek M. Toward a Morphodynamic Model of the Cell. IEEE Signal Processing Magazine, New York: IEEE, 2015, vol. 32, No 1, p. 20-29. ISSN 1053-5888. 2015. doi:10.1109/MSP.2014.2358263.

Topic: Cell Tracking in Microscopy

Supervisor: assoc. prof. Pavel Matula
Area:Biomedical image processing

The latest results on comparison of cell-tracking algorithms [1] indicated that the state-of-the-art algorithms are still, despite the very good results for some datasets, in general far from the demanded outcome. Especially, the development is necessary for scenarios with low signal-to-noise ratio or low contract ratio or for tracking cells with more complex shapes or textures. Large 3D data sets, such as those of developing embryos, present additional challenges. Not only do such videos show very high cell densities in later frames, the size of the image data itself causes very long runtimes. The aim of this research topic is to contribute to the solution of these chalenging problems.

[1] Ulman et. al. An objective comparison of cell-tracking algorithms, Nature Methods 2017

Topic: Generative Modeling in Biomedicine

Supervisor: doc. RNDr. David Svoboda, Ph.D.
Area: Biomedical image processing

The latest research in the field of biomedical image synthesis is driven by deep learning-based methods which mostly utilize so-called generative models (e.g. generative adversarial networks or variational autoencoders). The simulation frameworks that use the generative models have already attracted attention and are becoming more and more popular as they (are able to) produce sufficiently plausible results in large quantities. Their use is however limited by the complexity of the underlying neural networks that rapidly grows with the dimensionality of the input image data. The aim of this research topic is to study the properties of individual generative models, choose the suitable one for use in the field of virtual live cell imaging, and analyze the quality of the computer-generated image data.

Relevant sources: Proceedings of 4th international workshop on Simulation and Synthesis in Medical Imaging (SASHIMI).