Marie Skłodowska-Curie Doctoral Training Network – COMBINE

Project: Coupled Problems for Decarbonization in Industry and Power Generation (COMBINE)

The Marie Skłodowska-Curie Doctoral Training Network COMBINE brings together 17 academic institutions and 14 industrial partners across Europe. The network addresses key challenges in fluid–structure interaction (FSI) relevant to energy, process, and materials engineering, with a strong focus on:

• Advanced numerical modelling and simulation

• Experimental methods and sensor technologies

• Data analysis of real-world monitoring data

• Artificial intelligence and machine learning for engineering applications

Researchers in COMBINE benefit from interdisciplinary, international, and inter-sectoral training, including secondments at leading academic and industrial partner institutions across Europe.

Position Description 
Title: Vibration and Strain Monitoring Using Multi-Sensor Data Fusion and Artificial Intelligence

This project focuses on the analysis and monitoring of fluid–structure interaction phenomena using multi-sensor data fusion and AI-based methods. The research will combine advanced sensing technologies with machine learning to improve system understanding, imaging quality, and defect detection in complex engineering systems.

The research will address the following topics: 

• AI-assisted reconstruction and imaging of fluid flow regimes using sensor and tomography data, including:

  • X-ray computed tomography (CT)

  • Electrical impedance tomography (EIT)

  • Contactless inductive flow tomography

  • Distributed fibre-optic sensing

• Machine learning and deep learning methods for vibration and FSI data analysis, including:

  • Noise reduction and data enhancement

  • Pattern recognition

  • Identification of critical system parameters

• Analysis of composite material manufacturing processes (e.g. fibre–resin interaction, air entrapment) using CT and high-resolution optical imaging combined with AI-based image analysis

• Development of AI-based acoustic methods for vibration monitoring and defect detection in composite structures

Candidate Requirements: 

• Master’s degree (or equivalent) in engineering, physics, applied mathematics, computer science, or a closely related field

• Strong background in artificial intelligence and machine learning, including:

  • Supervised and unsupervised learning

  • Deep learning architectures (e.g. CNNs, autoencoders)

  • Model training, validation, and performance evaluation

• Hands-on experience applying ML/AI methods to experimental or sensor data, such as:

  • Time-series, vibration, or acoustic data

  • Imaging or tomography data

  • Multi-sensor data fusion

• Programming proficiency in Python, with experience using:

  • Scikit-learn

  • TensorFlow and/or Keras (or equivalent frameworks)

• Experience in data preprocessing, feature extraction, and model interpretation

• Ability to work independently and collaboratively in an international research environment

• Good written and spoken English

Desirable (but not mandatory): 

• Experience in signal processing, vibration analysis, or acoustics

• Familiarity with tomography, imaging techniques, or inverse problems

• Experience with large experimental or real-world datasets

• Knowledge of physics-informed machine learning or explainable AI

What we offer:

• Full-time position with €3,600 gross/month, plus additional allowances (mobility, living, research, training, networking)

• Excellent research environment at one of the leading technical universities in the Baltic States

• Supervision by internationally recognised academic and industrial experts

• Network-wide training events and local courses in technical and transferable skills

• International secondments at academic and industrial partner institutions across Europe

• The candidated will be offered a possibility to become a PhD student at Kaunas University of Technology (KTU) with the same research topic. Upon admission to the doctoral programme, the candidate will additionally receive a doctoral scholarship.
  

Application:

Applicants are invited to submit:

• CV

• Cover letter

Application deadline: 30 April 2026

The anticipated start date of the position is September 2026, subject to the completion of all recruitment, admission, and contractual procedures.

For further information, please contact:

Vykintas Samaitis 

vykintas.samaitis@ktu.lt

Eligibility Criteria (MSCA Rules) 

The candidate recruited in the COMBINE project must be a Doctoral Researcher and undertake transnational mobility (secondments, trainings, conferences). The candidate must be in the first four years from the date when the researcher obtained the degree entitling him or her to embark on a doctorate (e.g. master degree). It will be counted backward from the date of recruitment (in this case 01.09.2026). No doctoral degree has been awarded during these four years. The candidate can be of any nationality. The EU Mobility Rule applies. That is, the candidate must not have resided or carried out her/his main activity (work, studies, etc.) in the host country for more than 12 months in the 3 years immediately before the recruitment date. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.