Informatics Engineering

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

Double degree programme with the University of Bologna, Italy

Informatics Engineering is a branch of computer science, focused on effective solution of practical computation and information processing problems with application of scientific knowledge. This programme of doctoral studies aims to prepare future scientific leaders in the scientific field of Informatics Engineering. Each doctoral student who aims to acquire doctoral degree has to complete doctoral studies in the scope of 30 credits, conduct original scientific research in the scientific field of Informatics Engineering and develop new scientific knowledge, present the results of his/her scientific research in national and international scientific conferences and scientific journals, write and orally defend his/her doctoral dissertation. Programme provides doctoral students with a possibility to complete one study module at the foreign academic institution or participate in summer camp for doctoral students organised abroad and actively conduct the most advanced scientific research at the integrated centre of science, entrepreneurship and studies of Santaka valley since the first day of their studies. Awarded doctoral degree proves that doctoral student acquired a deep academic education and is able to conduct original scientific research in the field of Informatics Engineering.

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Topic title	Possible scientific supervisors	Source of funding
Generative adversarial network usage in motion matching	prof. dr. Rytis MASKELIŪNAS	state non-funded	Research Topic Summary. In current digital era, animations and their usage are an integral part of digital entertainment. Also, motion blending is one of the areas little touched by animation. Research on how deep neural networks could contribute to motion-tuning of animations remains relevant. The aim of the research is to create an algorithm that would be able to dynamically adapt and adjust motion animations to a specific situation, by analyzing various features on the fly, like an object’s reaction to force, motion or other vectors in a certain matrix, thus distancing itself from the limitations found in a classic fixed animations. Prerequisites: Artificial Intelligence, Object Oriented Programming, animation methods, game technologies.
Deep learning based Dynamic motion synthesis for on-the-fly skeleton animation	prof. dr. Rytis MASKELIŪNAS	state non-funded	Research Topic Summary. The main goal of this topic is a deep learning based dynamic motion generation system based on motion fields, for responsive and accurate real-time skeleton animation. This topic’s novelty is a focus on a method to generate enhanced animated movements more naturally and interactively by utilizing "on-the-fly" programming animation, combining artificial intelligence, biomechanics, physics and numerous other parameters available during run-time. Prerequisites: Artificial Intelligence, Object Oriented Programming, animation programming.
A study of Virtual Reality application for learning systems	prof. dr. Tomas BLAŽAUSKAS	state non-funded	Research Topic Summary. A large proportion of people have a dominant kinesthetic sensory preference. Besides, human learning depends on the environment around them. The emergence of high-quality and relatively inexpensive virtual reality devices, such as HTC Vive, Oculus Rift, Oculus Go, etc., has made it possible to realistically simulate the surrounding environment and simulate real-world activities, thus opening up opportunities for kinesthetic distance learning. The objectives of the research are: to analyze the methods of using virtual reality for learning; to develop the concept of a learning system that incorporates virtual reality; to propose a model of a multimodal system; to offer ways of providing lectures in virtual reality and ways to learn by using it; evaluate the effectiveness of using virtual reality for learning. The expected results are the proposed concepts, models, and practical implementations used for learning using virtual reality and the experimental evaluation of the proposed solutions.
Cryptographic Algorithms Specification Methods for Power Consumption Forecasting and Management in Mobile Devices	prof. dr. Jevgenijus TOLDINAS	state non-funded	Research Topic Summary. The number of mobile phone users in the world is expected to pass the five billion mark by 2019. Today mobile phone in our pocket is a high performance computer with gigabytes of RAM, processor that has two, four or more cores, Wi-Fi, Bluetooth, display, etc. All of mentioned devices consume battery energy. Unfortunately, while technical parameters of mobile phones growths quickly, battery lifetime remains very short and users must charge their phones more and more frequently. BYOD (bring your own device) is the increasing trend when employee brings their owned smartphones, tablets or laptops to their workplace. That is why prediction of energy consumption for devices with battery is an important issue. Main purpose of the dissertation: 1) To propose novel models, methods and software tools for energy consumption forecasting at application level; 2) To specify how much cryptographic algorithms consume the power of a mobile device. New results are expected: 1) Common models, specialized methods that assess the variability of the criteria and enable prediction, management of energy consumption at the application level; 2) Realization of specialized methods - software components that enable users to evaluate the energy consumption of cryptographic algorithms in relation to other criteria; (3) Adaptation of specialized methods for the flexible use of mobile devices for energy forecasting.
Preventive system maintenance using process mining and anomaly detection	prof. dr. Evaldas VAIČIUKYNAS	state non-funded
Machine learning models for solving object shape recognition problems	doc. dr. Armantas OSTREIKA	state non-funded	Research Topic Summary. When applying artificial intelligence algorithms, it is important to properly select criteria that combine the essential properties of the object or phenomenon under consideration, and thus to create an appropriate criteria/parameter model that allows unambiguous identification and classification of object state changes. In computer vision tasks, the creation of such models is not a trivial task, as visual information is enormously diverse and, moreover, changes dynamically. Properly designed and implemented, such models serve for high-level decision-making using a digital image or video data. These solutions are designed to automate tasks that can only be performed by a human. Computer vision systems are used in many fields, including manufacturing, medical, traffic monitoring, security systems, etc. Computer vision methods allow automatic analysis of important information in the processing of large data flows. Artificial intelligence algorithms in this case are an integral part of solving computer vision problems. The proposed topic will aim to improve existing common methodologies by focusing on specific products and real-world processes for greater efficiency and reliability, adapting and improving machine learning methods in Decision trees, K nearest neighbors, Naïve Bayes, Support vector classifiers, Direct propagation neural networks, and Convolutional neural networks. For more details please contact the supervisor of the topic.

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Application procedure

Mandatory documents for admission:

Filled in application form (online on apply.ktu.edu)
Official legalised Bachelor’s and Master’s diplomas or a higher education degree equivalents and academic transcripts (translated into English or Lithuanian and notary verified)
Curriculum Vitae
Recommendations by two researchers of the relevant science field (one of the recommendations must be written by the supervisor of the research topic that the candidate is applying to)
A research proposal on the topic selected for admission
A consent of the supervisor of the topic to which the candidate is applying, to supervise the candidate if he/she is invited to studies
A list of the research works (this list is submitted with full bibliographic description) along with copies of the 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, K. Donelaičio g. 73, Kaunas, Lithuania or online)

Signing a study agreement (if invited to study)

Cycle	Third cycle
Language	Lithuanian, English
Duration	4 y.
Degree awarded	Doctor of Science Dvigubas laipsnis

Research experience Research experience in ten-point grading system (The evaluation of the research experience includes the candidate’s scientific publications, other research experience, scientific qualifications, the compliance of the scientific publications and research experience with the topic of doctoral studies)	0,4
Weighted grade point average of the Master’s diploma supplement	0,3
Motivational interview, including evaluation of the knowledge of a foreign language	0,2
Research proposal	0,1

Informatics Engineering

Partners

Research topics

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Entry requirements

Minimum requirements

Application procedure

Selection criteria and their weighted coefficients

Study programme modules