Hybrid vibro-acoustics modelling for the auralization of EV components

Organisation/Company KU LEUVEN Research Field Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Application Deadline 30 Sep 2026 - 23:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number BAP-2026-579 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The recruited doctoral candidate will perform research based on the following objectives:

• Develop a physics-based baseline time domain model for vibro-acoustic components using FEM/BEM and reduced-order modelling techniques.
• Fuse sparse microphone or accelerometer data with model prediction enabling robust real time field reconstruction. Within this objective, techniques such as dynamic reduced order models, Kalman filtering and Physics Informed neural operators/networks will/can be employed.
• Exploit the reconstructed field to design a calibration (neural) model that bridges the gap between the physics-based prediction and the corrected field, ensuring physical interpretability and data adaptivity. Operator inference strategies can/will be employed to infer the properties of such a model.
• Extend the methods to a parametric calibration model through new material properties, new geometric configurations and sound sources through sophisticated interpolation strategies and/or transfer learning, enabling parametric response evaluation across vehicle assemblies.
• The developed strategies will be employed for the auralization of an existing electric vehicle.

The required funding has already been secured through the starting funds of Prof. Panagiotopoulos, ensuring a fully funded four-year PhD trajectory. Nevertheless, candidates with exceptionally strong profiles will be strongly encouraged to make use of the initial phase of the project to apply for an individual PhD fellowship through the Research Foundation – Flanders (FWO), further strengthening their academic track record and research independence.

The candidate will become part of the LMSD with a legacy on novel noise and vibration technique development. The candidate will frequently interact with researchers in other campuses (e.g. campus Heverlee, campus Diepenbeek) who work on noise and vibration research topics, while it is expected to contribute to the establishment and further development of noise and vibration research activities at Campus De Nayer. In this context, the candidate is expected to demonstrate a strong sense of initiative, ownership, and responsibility, and to actively support the growth and consolidation of the local research activities and collaborations.

If you recognize yourself in the story below, then you have the profile that fits the project and the research group:

• I have a master degree in engineering, physics or mathematics and performed above average in comparison to my peers. I am not in possession of a doctoral degree at the date of recruitment.
• I am proficient in written and spoken English.
• During my courses or prior professional activities, I have gathered some basic experience with the basic physical principles of acoustics and the related numerical modelling techniques, such as the Finite Element Method (FEM), Boundary Element Method (BEM), and/or with the basic principles of Machine Learning and/or Model Order Reduction or I have a profound interest in these topics.
• During my courses or prior professional activities, I have gathered experience in vibro-acoustic measurement techniques or I have a profound interest in this direction.
• I demonstrate a strong sense of initiative and responsibility, while also being an effective and collaborative team player.
• As a PhD researcher of the KU Leuven LMSD division I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
• Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
• In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
• I feel comfortable to work as a team member and I am eager to share my results to inspire