DCs

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Interested in becoming a PATRON Doctoral Candidate? Check out our Vacancies.

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Konstantinos Velis

Konstantinos, born and raised in Athens, Greece, holds an Integrated Master’s degree in Mechanical Engineering and Aeronautics (2025) from the University of Patras, Greece. His sub-specialization is Design and Manufacturing, with a focus on Stochastic Signals and Systems and Structural Health Monitoring. He completed his diploma thesis, titled “Vibration-Based Dynamics Identification and Damage Detection via Random Coefficient Model-Based Methods in a Population of Structures under Stochastic Uncertainty”, and graduated with excellence in April 2025.

In June 2025, Konstantinos began his PhD at KU Leuven with the research title “Multisensory Monitoring of Drivetrains Based on Advanced Signal Processing Indicators and Artificial Intelligent Tools”. He is doctoral candidate No. 1 in the PATRON international consortium, funded by the Marie Skłodowska-Curie Actions programme.

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Evenela Maria Dima

Evenela Maria was born and raised in Athens, Greece. She earned a Bachelor's degree in Mechanical Engineering in 2022 from Sapienza University of Rome and a Master's degree in Mechatronic Engineering in 2025 from the University of Modena and Reggio Emilia. During her Master's studies, she completed an internship at CNH Industrial in Modena, Italy, where she developed her thesis titled "Correlation and Validation of Virtual Models of Off-Highway Vehicles with Experimental Data." The primary objective was to perform correlation and validation of a tractor plant model using experimental data collected directly from a real tractor. The goal was to increase the fidelity of the dynamic simulator to ensure accurate comfort and feel on the platform, making it available for future development, testing, and tuning.

Starting in June 2025, she will pursue a PhD at KU Leuven with the project "Physics Inspired Machine Learning Techniques for Condition Monitoring of Drivetrains." She is Doctoral Candidate number 2 within the PATRON program, funded by the Marie Skłodowska-Curie Actions.

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Thato Sibanda

Thato Sibanda was born in South Africa. He obtained his Bachelor’s degree in 2020, Honour’s degree in 2021, and master’s degree in 2023, all in Mechanical Engineering from the University of Pretoria, South Africa. He is the author of "Cyclomap: A new phase-cycle analysis to study the kinematics of gears and bearings" (MSSP, 2023) and "Synchronous median instantaneous power spectrum-gram (SM-IPSgram): A filter banks decomposition for identifying informative frequency band and a weighting function"

(MSSP, 2024). His current research interests include the development of signal-processing techniques in mechanical applications.

In 2025, Thato began his PhD at INSA-Lyon, France. He is Doctoral Candidate number 3: his PATRON project, entitled "Physically-driven design of health indicators for diagnosis and prognosis" is under the Marie Skłodowska-Curie Actions program.

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Carlos Miranda

Carlos originated from Colombia. He holds a Master’s degree in Mechanical Engineering (2020) from the National University of Colombia, he completed the degree project titled “A comparative study of the ISO 6336 and ANSI/AGMA 2001-D04 standards for the load capacity calculation of involute cylindrical gears” by means of a side-by-side analysis focused on the behavior of the safety factors against both contact and bending failure. Over the years, his work has mainly focused on the development of mechanical transmission systems in both industrial and academic fields.

In September 2025, he joined Luleå University of Technology to start the position as a Doctoral Candidate, He is doctoral candidate no 4 within the PATRON project funded by the Marie Skłodowska-Curie Actions programme. The research is titled “Establishment of a lubrication quality parameter” and aims to develop a new lubricant quality measure to estimate the risk of failure in mechanical systems incorporating elastohydrodynamically lubricated contacts and connect the findings to condition monitoring practices.

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Yunbo Hao

Yunbo Hao was born and raised in China. He obtained a Master of Science degree with a major in mechanical engineering from Linköping University, Sweden, in 2020. Cooperating with Epiroc AB, he successfully accomplished the degree project—Evaluation of System Architecture Using Programmable Hydraulic Pump. After graduation, he worked in Volvo Construction Equipment as a technical manager, where he was responsible for the design and development of the latest generation of electric-mobility transmission modules.

In 2024, he joined the University of Ljubljana as a researcher at the Faculty of Mechanical Engineering, in the Laboratory for Tribology and Interface Nanotechnology (TINT). His current research focuses on film thickness in elastohydrodynamic lubrication, solid–liquid interface, and diamond-like carbon coatings. He is the Doctoral Candidate number 5 in the PATRON project funded by the Marie Skłodowska-Curie Actions programme.

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Imen Tounsi

Originally from Tunisia, she began her academic journey with preparatory studies in Mathematics and Physics at the Preparatory Institute for Engineering Studies of Sfax. She then pursued a multidisciplinary engineering education at the Polytechnic School of Tunisia, where she specialized in Signals and Systems.For her graduation project, she joined the Measurement and Sensor Technology Laboratory at Chemnitz University of Technology in Germany. Her thesis, titled "Hand Gesture Recognition Through Compressive Sensing and Electromyography Signal Processing," focused on developing a compressive sensing framework and a deep learning model to efficiently compress electromyography (EMG) signals and classify them for accurate hand gesture recognition.

In February 2025, she embarked on her PhD journey at Safran Tech in collaboration with the LAPSI laboratory at Jean Monnet University in Saint-Étienne, France. Her research, funded by the Marie Skłodowska-Curie Actions programme, is titled "Signal Processing and Neural Networks for Planetary Gear Signal Analysis." This work aims to improve aircraft engine health monitoring by creating novel health indicators for planetary gear systems. She combines advanced signal processing techniques with neural networks, particularly autoencoders, to decompose complex vibration signals and enhance fault detection capabilities.