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Joint doctoral studies with Vytautas Magnus University and Vilnius Gediminas Technical University (coordinating institution: Kaunas University of Technology)

Informatics is the field of science based on Computer Science and Computational Science. The research is carried out using mathematical models of analysed object or system, creating corresponding simulation algorithms and obtaining the results important for science and practice. Doctoral students investigate the research fields of data analysis, signal and image analysis, simulation modelling, computational intelligence, physically-based behavior, development and analysis of general dynamic models, cryptography algorithms, systems formalization and high-level transformation of specifications, etc. Some of the research fields can as also be categorized in the scientific field of Informatics Engineering, depending on the dominant aspect of the model’s implementation – mathematical model of a system or engineering-architectural aspect.

The aim of doctoral studies in Informatics is to educate scientists capable to develop and creatively apply the methods of information technology for creation and analysis of the mathematical models of real world objects and systems aiming to find solutions to important scientific and practical problems.

Cycle Third cycle
Language Lithuanian, English
Duration 4 y.
Degree awarded Doctor of Science
  • Vytautas Magnus University
  • Vilnius Gediminas Technical University

Research topics

Topic title Possible scientific supervisors Source of funding
Research and implementation of methods for brain-computer interface on the base of deep neural networks
prof. dr. Vacius JUSAS state-funded
Research and implementation blockchain-based reputation model of professor
prof. dr. Vacius JUSAS state-funded
Artificial intelligence based model for predicting symptoms of early Dementia though the analysis of multimodal feature signals
prof. dr. Rytis MASKELIŪNAS state-funded
Unsupervised, Deep Learning-Based model for Detection of Failures in Industrial Machinery
prof. dr. Rytis MASKELIŪNAS state-funded
Anomaly detection algorithms based-on unsupervised learning methods
doc. dr. Agnė PAULAUSKAITĖ-TARASEVIČIENĖ state-funded
Self-supervision for weakly-supervised medical image segmentation
doc. dr. Tomas Iešmantas state-funded
Hierarchicity-based (self-similar) heuristic algorithms for combinatorial optimization problems
prof. dr. Alfonsas MISEVIČIUS state-funded
Geometric iterative methods for data approximation using adaptive hierarchical splines
doc. dr. Svajūnas Sajavičius state-funded
Design and analysis of long series dependence estimators
doc. dr. Tomas RUZGAS state-funded
Design and analysis for statistical hypotheses tests
doc. dr. Tomas RUZGAS state-funded
Creation and investigation of the payment system based on the blockchain technology with the elements of non-commuting cryptography
doc. dr. Aleksejus MICHALKOVIČ state-funded
Deep learning assisted subsurface data lab
doc. dr. Mayur Pal state-funded
Reactive transport modelling in presence of discrete fracture networks in porous media
doc. dr. Mayur Pal state-funded
Computer vision algorithms for solving object shape recognition problems
doc. dr. Armantas OSTREIKA state-funded
Confidential and verifiable transactions system in blockchain
prof. dr. Eligijus SAKALAUSKAS state-funded
New algorithms for the stabilization and control of chaotic systems
prof. habil. dr. Minvydas Kazys RAGULSKIS state-funded
Multicriteria decision making in finance using explainable artificial intelligence
doc. dr. Audrius KABAŠINSKAS state-funded
Machine learning and information integration research for disease prediction and health care
prof. dr. Robertas ALZBUTAS state-funded
Artificial intelligence for monitoring and risk analysis of battery aging
prof. dr. Robertas ALZBUTAS state-funded
Download the list




Entry requirements

Minimum requirements

Persons with a Master’s Degree or equivalent degree of higher education may participate in an open competition for admission to doctoral studies.

Applicants to the doctoral field of science are accepted by competition according to the competition score. Minimum Competition Score 7.0


Application procedure

Mandatory documents for admission:

  • Filled in application form (online)
  • Official legalised Master’s diploma or a higher education degree equivalents and academic transcripts (translated into English or Lithuanian and notary verified) and the certificate of recognition if higher education qualification acquired abroad (see academic recognition)
  • Curriculum Vitae
  • Recommendations by two researchers of the relevant science field
  • A list of the research works (this list is submitted with full bibliographic description) along with copies of the research works (or a research paper on the topic (-s) consistent with the dissertation topic (-s) indicated in the candidate’s application, if a candidate does not have any research works)
  • A copy of the passport personal data page, or a copy of the ID card
  • A copy of the receipt of the application fee payment (40 EUR for EU citizens, and 100 EUR for non-EU citizens)
  • A certificate issued by IELTS, TOEFL, CERF or any other competent institution within the last 2 years certifying the level of the English language if the candidate is not a graduate of the first or second cycle studies conducted in the English language

Participation in motivational interview  (Kaunas University of Technology, Studentų g. 50, Kaunas, Lithuania or online)

Signing a study agreement (if invited to study)


Selection criteria and their weighted coefficients

Weighted grade point average of the Master’s diploma supplement


Research experience

Research experience in ten-point grading system (the evaluation of the research experience includes the candidate’s scientific publications or the research paper, other research experience, scientific qualifications, the compliance of the scientific publications and research experience with the topic of doctoral studies)


Recommendations by scientists


Motivational interview, including evaluation of the knowledge of a foreign language





Study programme modules