PhD position in multilevel interacting particle methods Bayesian inversion

Organisation/Company KU LEUVEN Research Field Mathematics » Computational mathematics Researcher Profile First Stage Researcher (R1) Country Belgium Final date to receive applications 30 Apr 2026 - 23:59 (UTC) 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
- Is the Job related to staff position within a Research Infrastructure? No

To reliably use simulation-generated predictions in science and engineering, one needs trustworthy mathematical models that are calibrated to measurement data. We are motivated by applications in engineering in which the system models are partial differential equations (PDEs) with potentially infinite-dimensional (e.g., space-dependent) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive inverse problems.

The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost.

In this project, we will focus on increasing the computational efficiency of interacting particle methods for Bayesian inversion when including model error in a multilevel hierarchy. As model problem, we will consider the inference of parameters in applications arising from structural mechanics.

The research will be carried out in an international team of numerical analysts le the envisaged work is generic (not tied to a specific application), the project will benefit from a long-standing and fruitful collaboration with the Structural Mechanics group in the Department of Civil Engineering.

• Candidates must hold a master degree in Mathematical Engineering or (Applied) Mathematics (or equivalent).
• Candidates should have a solid background in numerical methods for differential equations, simulation of stochastic processes and/or optimization.
• Candidates should have experience with programming of scientific software.
• Excellent proficiency in English is required, as well as good communication skills, both oral and written.
• A high-level and exciting international research environment.
• A supportive and collaborative team in which you can develop know-how and expertise in state-of-the-art simulation methods.
• The opportunity to build up research and innovation skills that are essential for a future career in research and development, both in an industrial and academic context.
• Funding is secured for four years.

Submit your application, including

Applications will be considered as soon as they are received, and the opening will be closed as soon as a suitable candidate is hired. The formal Final date to receive applications is April 30, 2026.

For more information please contact Prof. dr. ir. Giovanni Samaey, tel.:  mail:

• Candidates must hold a master degree in Mathematical Engineering or (Applied) Mathematics (or equivalent).
• Candidates should have a solid background in numerical methods for differential equations, simulation of stochastic processes and/or optimization.
• Candidates should have experience with programming of scientific software.
• Excellent proficiency in English is required, as well as good communication skills, both oral and written.