Special PhD positions at the Department of Visual Computing

2023

Department of Visual Computing, Faculty of Informatics, Masaryk University, announces an open call for one PhD position starting from the Autumn 2023 term, with applications in the following areas:

The deadline for application is May 14, 2023.

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 a stipend of net value at least 32 000 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.

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 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.

Topic: Playful Approaches for Creating Awareness About Pro-Sustainability Behaviour

Supervisor: assoc. prof. Simone Kriglstein
Area:Human-Computer Interaction, Games, Psychology

The awareness about sustainability values and a sustainable way of life, in the sense of having empathy and taking care of the environment, is an essential topic that will influence how we will live in the future. One question is how people can be influenced to become more aware of environmental sustainability. Since technology is considered as a means to simplify people's lives, it can also be used as a tool to support people's awareness about these aspects. The goal of this PhD topic is to investigate how games and playful approaches can be used in this context and to develop a kind of framework by demonstrating different ways to support awareness about sustainability issues. It can reach from learning games about sustainability (e.g. [1] ) to giving a new meaning to vintage objects that are no longer used by reusing them in a game and thus creating new game experiences (e.g. [2]).

[1] Raluca Chisalita, Markus Murtinger, and Simone Kriglstein. 2022. Grow Your Plant: A Plant-Based Game For Creating Awareness About Sustainability Behaviour by Using Renewable Energy. In Extended Abstracts of the 2022 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '22). Association for Computing Machinery, New York, NY, USA, 177–182.
[2] Barakura Thierry Tuyishime and Simone Kriglstein. 2021. Rrrring & Play: Using a Rotary Dial Telephone as Game Controller. In Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '21). Association for Computing Machinery, New York, NY, USA, 382–385.

Topic: Attention-based processing of large-scale microscopy images

Supervisor: doc. RNDr. Petr Matula, Ph.D.
Area: Biomedical image processing

Cell segmentation and tracking are significant problems in many life science studies. As the data sizes gradually increase, there is a pressing need for efficient and practically usable methods. Processing of large-scale image datasets is complicated for two main reasons: (1) current deep learning methods require a lot of memory and computing power, in particular in the training phase, and (2) high-quality 3D annotations, which could be used for training, are lacking. The main purpose of this topic is to develop new methodologies, algorithms, and representations based on attention mechanisms for better cell segmentation and tracking in large-scale data. The student will work mainly with live-cell microscopy images available in the Cell Tracking Challenge [1].

Relevant sources: [1] Cell Tracking Challenge.

Topic: Morphology-aware Modeling in Biomedicine

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

The current research in the field of medical and biomedical image synthesis is driven by deep learning-based methods which utilize generative models (various modifications of basic concepts originating from generative adversarial networks and variational autoencoders). The simulation frameworks that use generative models have already attracted great attention and are very popular as they are able to produce sufficiently plausible results in large quantities. Their efficiency goes hand in hand with proper understanding of augmentated data and latent space that controls the behavior of these method. The aim of this research topic is to study the properties of image data as well as latent space. The research field is tightly connected to the topic called Explainable AI.

Relevant sources: International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI).