Cell Behaviour Studies using Machine Learning – Opportunity at Masaryk University – Brno Czechia.

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  • Post Date: November 9, 2021
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Job Overview

Cell Behaviour Studies using Machine Learning


Machine Learning - fortiss

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 a 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). Afterward, 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 the 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.

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.


The applications will be evaluated by the department committee, whose members will choose the best applicant. The announced PhD position is funded with a stipend of net value at least 29 000 CZK per month (this amount consists of the standard doctoral stipend and the extra departmental supplement of 10 000 CZK per month). 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 prospective 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

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.




Job Detail
  • Offered SalaryNot Specified
  • Career LevelStudent
  • ExperienceNot Specified
  • GenderBoth
  • INDUSTRYEducation
  • QualificationMaster's Degree(M.Sc.)
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