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.

Topic title	Possible scientific supervisors	Source of funding
Development and research of the autonomous control system of the bioreactor	prof. dr. Renaldas URNIEŽIUS	state-funded	Research Topic Summary. The doctoral topic is related to developing new-generation software sensors and bioprocess management tools and systems adopted by these sensors. The research group aims to use the latest biotechnological knowledge most efficiently, initially for the monitoring and controlling laboratory-scale continuous processes, and later by applying the developed algorithms and technical solutions in industrial-scale bioprocesses. Research results and engineering solutions can also become the basis for various start-up initiatives in the developing life sciences ecosystem.
Artificial and ambient intelligence application to simulation and smart design of optimal and reliable systems	prof. dr. Robertas ALZBUTAS	state-funded
Design for Explainability: Methods and Models for Explainable Artificial Intelligence Based Decision Support	prof. dr. Robertas DAMAŠEVIČIUS	state-funded	Research Topic Summary. With the surge of data science in many areas such as healthcare and finances, Artificial Intelligence (AI) techniques are employed to discover new knowledge, unveil hidden data patterns and behavior, generate new insights, and seek optimal strategies in decision-making. However, as the effectiveness of AI models has improved, so has their complexity increased to the point where the models' decision-making has become unintelligible and the models are regarded as black boxes. With the primary focus on improving their performance, the issue of explainability (or interpretability) has been largely neglected. Without comprehension of what the model has learned during training, it is impossible to ensure that the model will be devoid of biases or fairness, which is problematic from an ethical, societal, and legal standpoint. Any developed AI method or expert system should not only bring good results but also have good interpretability, i.e., let users know why he/she should trust in its decision. The existing AI methods generally lack good explainability and may lack trustworthiness and transparency in usage. Therefore, one of the key challenges should be achieving the explainability of AI in problem-solving and decision-making. The research aim is to propose, investigate and implement an explainable AI technique for decision support to solve real-world problems.
Application of deep learning techniques for dynamic images anomality detection	doc. dr. Arūnas LIPNICKAS	state-funded	Research Topic Summary. Recently emerged new artificial intelligence method called deep learning neural networks that has shown the ability to carry contextual analysis of video information. Such methods work perfectly with monolithic surfaces, but not jet applied in the food, textile and furniture industry with a patterned and mottled surfaces. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labelled training data for abnormal class. The aim of this work is to build one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to find dynamic images anomality.
Application of deep learning models for effective analysis of seabed imagery	prof. dr. Evaldas VAIČIUKYNAS	state-funded
Machine learning models for solving object shape recognition problems	doc. dr. Armantas OSTREIKA	state-funded	Research Topic Summary. When applying artificial intelligence (AI) 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 methods allow automatic analysis of important information in the processing of large data flows. AI 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 digital image acquisition, processing, analysis and interpretation methods, machine learning and AI methods including convolutional neural networks, hybrid convolutional network models, real-time object detection algorithms (YOLO), methods supported by autoregressive language models (GPT-3) etc. Focusing on real examples of provided services or production processes, the aim will be to apply and test the developed models and methodologies in practice. Examples of applications include computer vision systems that measure and count products or blanks, estimate their weight, shape, or volume, and inspect objects at high speed based on their predefined characteristics. In this research, we expect to overcome operative restrictions that hamper the adoption of new algorithms. It is expected to modify existing methodologies and to implement novel research ideas based on AI and new heuristic approaches.
Cardiovascular Disease Prediction Using Machine Learning Algorithms	prof. dr. Agnius LIUTKEVIČIUS	state-funded	Research Topic Summary. Cardiac Magnetic Resonance (CMR) is the most effective tool for the assessment and diagnosis of heart disease, while heart failure is the leading cause of death in the world. Software tools that leverage artificial intelligence already help radiologists and cardiologists in the assessment of heart disease, but their lack of transparency is a problem. This doctoral thesis will investigate whether it is possible to discover concepts that represent different cardiac conditions, using explainable AI (XAI) methods. The research will be based on an interpretable method to extract important underlying features for the diagnosis of cardiac disease from MRI (Magnetic Resonance Imaging) data.

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Application procedure

Mandatory documents for admission:

Application indicating the science field, the topic and the form of funding (online)
Official legalised Master’s diploma with the diploma supplements in the fields of Physics, Mathematics or Informatics in the study fields of Technologies or Physical Sciences 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
Curriculum Vitae
Recommendations by two researchers of the relevant science field (one of the submitted recommendations by scientists has to be written by the supervisor of the topic)
A research proposal on the selected topic
The supervisor’s written agreement to supervise the applicant if the latter is invited to study
A list of the research works with a full bibliographic description and copies thereof; applicant is required to have published at least one scientific publication (or to present a publication which has been accepted for publication, and, in this case, submit a copy thereof)
A copy of the passport page with personal data, 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)

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

Weighted grade point average of the Master’s diploma supplement	0,3
Research experience Publications evaluated according to the current regulations for the annual evaluation of research and experimental development and artistic activities of universities and science institutes approved by the order of the Minister of Education and Science of the Republic of Lithuania, other research experience and scientific qualifications, compliance of scientific publications and research experience with the topic of doctoral studies.	0,4
Research proposal on the selected topic for admission	0,1
Motivational interview, including evaluation of the knowledge of a foreign language	0,2

Informatics Engineering

Partners

Research topics

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Entry requirements

Minimum requirements

Application procedure

Selection criteria and their weighted coefficients

Study programme modules