PhD positions at the Department of Machine Learning and Data Processing

Department of Machine Learning and Data Processing, Faculty of Informatics, Masaryk University, announces an open call for one PhD position starting from the Spring 2022 term, with applications in the following areas:

The deadline for application is January 3, 2022.

General information

Perspective PhD students are expected to show their research background and skills in the area of the selected PhD topic. They should also be proficient in English; 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 an extra department stipend of 10 000 CZK per month (summing to 29 000 CZK with the standard faculty stipend in the Czech study programme). Successful applicants studying the English study programme will receive extra faculty stipend to equalize the state-supported stipend of the Czech programme. 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, motivation letter, description of study time plan and expected outcomes, 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: Intelligent Multilingual Man Machine Communication

Supervisor: doc. RNDr. Aleš Horák, Ph.D.
Area: Natural Language Processing, Knowledge Representation, Dialogue Management

Communication between a man and a computer program is one of the long-term goals of the NLP field. Current systems related to chatbot-style communication are able to respond in a satisfactory level to a broad range of questions, the background of such communications is, however, kept mostly on the lexical level. Recent results in the task of open domain question answering promise to bring adequate background to such dialogues.

The aim of the thesis is to combine the two approaches (general discussion robots and question answering systems) in a new approach with the concentration on multilingual environment. In the evaluation part, the thesis needs to offer new results (also) for languages other than the mainstream ones to prove its applicability to a broad spectrum of languages.

Publications relevant to the given topic can be found on the pages of the NLP Centre FI MU

Topic: Synthesis and Verification of Stochastic Systems Using Learning Methods

Supervisor: doc. RNDr. Tomáš Brázdil, Ph.D.
Area: Theoretical computer science, Formal Methods

We concentrate on analysis of systems that exhibit randomness and non-determinism. Randomness often stems from failures in physical components, unreliable communication, randomization, etc. Non-determinism naturally arises from underspecification, concurrency, etc. Such systems are typically modelled using Markov decision processes (MDPs), or stochastic games. These models have been widely studied for decades in various contexts such as engineering, artificial intelligence and machine learning (AI-ML), and, a bit more recently, formal verification. Recent results indicate that the field of formal verification may hugely benefit from results and methods developed in the framework of AI-ML. The aim of this PhD project is to further investigate the interplay between synthesis methods from AI-ML and formal verification of MDPs and stochastic games.

Topic: Anomaly detection and description

Supervisor: doc. RNDr. Lubomír Popelínský, Ph.D.
Area: Anomaly detection

Anomaly (outlier, rare event) analysis aims at finding anomalous instance in data. Anomalous means potentialy generated by other mechanism than majority of data. Detection, however, is only the first step of the analysis. The second looks for description and explanation of a found anomaly and it can be tightly connected with feature selection for anomaly detection.

The goal of this PhD research is to elaborate new methods for detection and description of anomalies. Research can be focused on a particular domain, e.g. text, structured data like graphs or sentences among others, and/or on a particular kind of anomalies, e.g. class-based anomalies or anomalies in logic.

Topic: Next Generation Clinical Decision Support System

Supervisor: Mgr. Vít Nováček, PhD, doc. RNDr. Aleš Horák, Ph.D.
Area: Medical Informatics, Clinical Decision Support

In recent years, advanced machine learning (ML) techniques such as deep, representation, and relational learning have been instrumental in a number of medical informatics breakthroughs (e.g. Stanford's dermatologist-level model for predicting melanoma in 2017). These advances have been enabled by growing maturity of various machine learning methods applicable to such use cases, and by vast volumes and numbers of life science datasets that have become available for machine processing. However, many crucial challenges remain unsolved in the field of machine learning and artificial intelligence (AI) applications in medicine. One of the most important under-researched areas is for instance explainability of AI-powered predictive models for clinical decision support. Machine-aided techniques for suggesting possible diagnosis or personalised therapies do exist nowadays, but their trustworthiness and applicability is often critically hampered by the inability of the models to explain their recommendations. Therefore, an advance in this area would not only be a possible academic breakthrough, but also a result with potentially vast societal and economic impact.

The aims of the prospective thesis are: