Measurement Engineering

The KTU PhD in Measurement Engineering is designed for young researchers who want to develop innovative technological solutions with high added value in the fields of space, transport, medicine, renewable and nuclear energy and other industries. These studies provide in-depth knowledge of measurement technologies. Research in this area of science includes the importance of quality control, non-destructive testing and ensuring the safety of people and the environment.
Measurement engineering is inextricably linked to modern technology, ensuring the accurate and reliable operation of advanced systems. Research in this field contributes to meeting the needs of industry and society, ensuring product quality, energy efficiency and human safety. The PhD programme develops new measurement methods and addresses the challenges of Industry 4.0. Measurement technologies play an important role in the increasing digitalisation of industry and the development of the next generation of smart devices.

about field of science

Topic title	Possible scientific supervisors	Source of funding
Innovations in ICP Wave Monitoring for Personalized Neurological Care	vyresn. m. d. dr. Yasin Hamarat	state-funded	Research Topic Summary. Traumatic Brain Injury (TBI) affects 69 million people annually, with new cases ranging from 100 to 330 per 100,000 each year. TBI is a major cause of death and disability, incurring significant healthcare and societal costs in working age group. Intracranial pressure waveforms hold crucial information about brain health, with potential applications beyond TBI. This proposal explores utilizing these waveforms to enable individualized, precise medical treatments. Multimodal monitoring of intracranial waves can enhance patient outcomes, promptly identify ischemic and hyperemic events, and minimize secondary neurological damage.
Automated Detection and Classification of Defects in X-ray Computed Tomography Using Machine Learning	prof. dr. Elena Jasiūnienė	state-funded	Research Topic Summary. X-ray computed tomography (CT) is becoming increasingly popular for visualising of the 3D internal structure of objects in various industries. In many cases visualisation of the structures is not sufficient, and automated detection and classification are also required. The scientific problem addresses the challenges and uncertainties associated with automated defect detection and classification from 3D CT data. Influencing factors such as material properties, beam hardening, filtration, tube current, number of projections, object placement, etc. must to be evaluated. In addition, the effectiveness of different machine learning algorithms in identifying defects will be evaluated. The objective of the research is to develop automated defect detection and classification algorithm in X-ray computed tomography data using machine learning.
Assessing the Reliability of Digital Biomarkers Derived from Wearable Health Technologies	prof. dr. Vaidotas Marozas	state-funded	Research Topic Summary. Relevance. The information technology revolution is transforming people's lives and wearable health technologies are already widely used to monitor cardiovascular and neurological diseases. Artificial Intelligence and Machine Learning (AI/ML) techniques are being applied to the diagnosis of cardiac arrhythmias, sleep disorders, depression, epilepsy, as well as glucose and blood pressure monitoring, allowing personalised treatment. Problem. Health monitoring outside the clinic faces challenges such as uncertain recording conditions and motion artefacts that make AI/ML models unreliable. Methodologies are needed that allow not only to record biomarkers but also to estimate their uncertainty, increasing the diagnostic reliability of wearable technologies. Aim - to develop and investigate methods to ensure the reliability of wearable health technologies. Expected results. The developed methodologies and algorithms will allow to estimate the uncertainty of digital biomarkers, increasing the confidence in wearable technologies, and will be applied to the monitoring of cardiac arrhythmias, sleep disorders and blood pressure.
Problems and development of non-invasive technologies for physiological monitoring of human brain and for neuroprotection of brain functions.	prof. dr. Arminas Ragauskas	state-funded
Application of Guided Lamb Waves for Characterization and Non-Destructive Testing of High Density Polyethylene (HDPE) Pipe Joint Systems	m. d. dr. Justina Šeštokė	state-funded
Remote metrology methods for monitoring the characteristics of measuring instruments	doc. dr. Paulius Kaškonas	state-funded	Research Topic Summary. The understanding of object characteristics, processes, phenomena, and other information collection is grounded in quantitative assessment and measurements, with the volume of such data rapidly increasing. Industrial automation and the development of advanced manufacturing methods drive a significant growth in the number of measuring instruments, making remote metrology increasingly relevant. This field focuses on reducing, optimizing, and automating the traditional, contact-based maintenance procedures for measuring devices, aiming to promote more efficient and less intervention-dependent management of maintenance processes. The importance of remote metrology is expanding across the fields of manufacturing, healthcare, energy, and environmental protection, as it enables real-time monitoring and analysis of metrological characteristics of devices, minimizing downtime due to metrological procedures (e.g., calibration in accredited laboratories or verification). Research and development efforts at the Kaunas University of Technology’s Faculty of Electrical and Electronics Engineering, specifically within the Institute of Metrology and the Department of Electronics Engineering, highlight the growing need for remote metrological monitoring procedures in industry. Advancing this field would involve developing new, legal and industrial metrology accepted methods to replace or supplement traditional and contact-based measurement procedures for assessment and monitoring of the metrological characteristics of measuring instruments 'in situ'.
Application of artificial intelligence methods for analysis of informative regions and measurement of their quantitative parameters within ultrasonic NDT and medical diagnostic images	prof. dr. Renaldas Raišutis	state-funded	Research Topic Summary. The scientific problem includes the informative analysis of ultrasound signals and diagnostic images and the quantitative interpretation of the results in order to detect internal defects and pathologies. The aim is to develop and investigate the methods of analysis of informative areas in ultrasound non-destructive testing and medical diagnostic images, providing opportunities for quantitative parameter measurement and automated classification of these areas (internal defects and various pathologies), using artificial intelligence methods. The research will use advanced research infrastructure with global relevance.

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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 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
A research proposal on the topic selected for admission (if the applicant applies to two topics, two individual research proposals have to be submitted)
A list of the research works with a full bibliographic description and copies thereof; if no research works by the applicant are presented, the applicant is required to present a research paper whose topic is consistent with the dissertation topic indicated in the applicant’s application (if the applicant applies to two dissertation topics, two individual research papers have to be submitted)
A copy of a valid personal document: a passport (for citizens of all countries) or an ID card (only for citizens of the European Union)
A copy of the receipt of the application fee payment (50 EUR for EU citizens, and 120 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

* The applicant can select no more than 2 (two) research fields and no more than 2 (two) topics per science field.

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	0,35
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)	0,4
Research proposal on the selected topic for admission	0,05
Motivational interview, including evaluation of the knowledge of a foreign language	0,2

Measurement Engineering

Contacts

Research topics

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Entry requirements

Minimum requirements

Application procedure

Selection criteria and their weighted coefficients

Study programme modules

Contacts