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Giovanni Samaey

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Postdoc in Interacting Particle Methods for Bayesian Inversion with Model Error KU Leuven in Belgium

I am offering a postdoc position in interacting particle methods for Bayesian inversion with model error at KU Leuven.

KU Leuven

Belgium

email-of-the@publisher.com

Feb 28, 2026

Keywords

Computer Science
Mathematics
Numerical Analysis
Stochastic Processes
Electrophysiology
Medical Science
Computational Mathematics
Computational Simulation
Sampling Methods
Statistics
Pde
Bayesian Inversion
Optimization
Applied Maths
Scientific Computing Software
Inverse-problems

Description

NUMA, the Numerical Analysis and Applied Mathematics section within the Department of Computer Science at KU Leuven, is offering a postdoctoral position focused on interacting particle methods for Bayesian inversion with model error. NUMA is a vibrant research group comprising 12 permanent staff and around 60 PhD and postdoctoral researchers, dedicated to developing advanced numerical algorithms and software for large-scale scientific and engineering problems. This project addresses the challenge of reliably using simulation-generated predictions in science and engineering, particularly when mathematical models—often partial differential equations (PDEs) with infinite-dimensional parameters—must be calibrated to measurement data. The research centers on computationally intensive inverse problems, where parameters and states are inferred from large, high-resolution datasets. The team aims to advance Bayesian computational methods for these ill-posed inverse problems, focusing on both increasing their validity and reducing computational cost. The specific goal of this postdoc is to enhance the validity of interacting particle methods for Bayesian inversion by explicitly including model error in the likelihood evaluation. The model problem will involve inferring parameters in phenomenological models for cardiac electrophysiology, leveraging a long-standing collaboration with the Department of Cardiovascular Imaging and Dynamics. The work will be applied in the context of cardiac excitation simulations, but the methods developed are intended to be generic and broadly applicable. Candidates should hold a PhD in Mathematical Engineering or Applied Mathematics (or equivalent), with a strong background in numerical methods for differential equations, simulation of stochastic processes, and/or optimization. Experience with sampling methods for Bayesian inversion and cardiac electrophysiological modeling is highly valued. Proficiency in programming scientific software and excellent English communication skills are required. The position offers a high-level international research environment, a supportive and collaborative team, and opportunities to develop expertise in state-of-the-art simulation methods. Funding is secured for two years, with the possibility of a third year depending on progress and available resources. The salary is competitive. Applications should include a letter of motivation, CV, diploma copies and transcripts, and contact information for 1-2 references. The formal deadline is February 28, 2026, but applications will be reviewed as received and the position will close once a suitable candidate is found. For further information, contact Prof. dr. ir. Giovanni Samaey at Giovanni.Samaey@kuleuven.be. KU Leuven is committed to diversity, inclusion, and equal opportunity, fostering an environment of open dialogue and respect for all backgrounds.

Funding

Available

How to apply

Submit your application including a letter of motivation, curriculum vitae, university diploma copies and transcripts, and names and contact information of 1-2 references. Applications are considered as soon as received and the opening will close once a suitable candidate is hired. Apply via the KU Leuven jobsite link provided.

Requirements

Candidates must hold a PhD 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. Specific experience with sampling methods for Bayesian inversion is highly appreciated. Experience with cardiac electrophysiological modeling is a plus. Candidates should have experience with programming of scientific software. Excellent proficiency in English and good oral and written communication skills are required.

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