Giovanni Samaey
Top university
3 months ago
PhD position in multilevel interacting particle methods for Bayesian inversion KU Leuven in Belgium
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Apr 30, 2026
Country
Belgium
University
KU Leuven

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Where to contact
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About this position
KU Leuven's NUMA (Numerical Analysis and Applied Mathematics) research division, part of the Department of Computer Science, is offering a fully funded PhD position focused on the development of multilevel interacting particle methods for Bayesian inversion. NUMA is a vibrant research environment with 12 permanent staff and around 60 PhD and postdoctoral researchers, dedicated to advancing numerical algorithms and software for large-scale scientific and engineering problems.
The project addresses the challenge of calibrating mathematical models—particularly those governed by partial differential equations (PDEs) with high-dimensional or infinite-dimensional parameters—using measurement data. Such inverse problems are computationally intensive, especially when high-resolution data is involved. The research will center on designing and analyzing efficient Bayesian computational methods for these ill-posed inverse problems, with a special emphasis on improving the computational efficiency of interacting particle methods within a multilevel framework that accounts for model error.
Applications of the developed methods will be drawn from structural mechanics, benefiting from NUMA's longstanding collaboration with the Structural Mechanics group in the Department of Civil Engineering. However, the research is fundamentally methodological and not tied to a single application domain. The successful candidate will join an international team of numerical analysts and have the opportunity to develop expertise in state-of-the-art simulation and computational techniques.
Eligibility: Applicants must hold a master's degree in Mathematical Engineering, (Applied) Mathematics, or an equivalent field. A strong background in numerical methods for differential equations, stochastic process simulation, and/or optimization is required, as is experience in scientific software programming. Excellent English proficiency and strong communication skills are essential.
Funding: The position is fully funded for four years, with a competitive salary and access to a supportive, collaborative research environment. The project offers excellent opportunities for professional development in both academic and industrial research contexts.
Application Process: Interested candidates should submit a letter of motivation (including a statement of skills and research interests), a curriculum vitae, copies of university diplomas and transcripts, and the names and contact information of 1-2 references. Applications are reviewed on a rolling basis until the position is filled, with a formal deadline of April 30, 2026. For further information, contact Prof. dr. ir. Giovanni Samaey at [email protected].
KU Leuven is committed to diversity, inclusion, and equal opportunity, fostering an open and respectful academic environment. For questions about accessibility or support, applicants are encouraged to reach out via the provided contact email.
Funding details
Available
What's required
Candidates must hold a master's degree in Mathematical Engineering or (Applied) Mathematics (or equivalent). A solid background in numerical methods for differential equations, simulation of stochastic processes, and/or optimization is required. Experience with programming of scientific software is expected. Excellent proficiency in English and good communication skills, both oral and written, are mandatory.
How to apply
Submit your application including a letter of motivation with a statement of skills and research interests, a curriculum vitae, university diploma copies and transcripts, and names and contact information of 1-2 references. Applications will be considered as soon as they are received. Apply via the provided KU Leuven jobsite link.
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