PhD Position: Numerical Analysis Inverse Wave Scattering

PhD Position:
Numerical Analysis for Inverse Wave Scattering

Radboud University is a denominational university originally established in 1923 as the Catholic University of Nijmegen.

Required background Research University Degree

Organizational unit Faculty of Science

Final date to receive applications 08 January 2026

How can we see what is hidden? And how much can we trust the reconstruction? Answering these questions requires pushing the limits of numerical analysis and computation for wave equations and inverse problems. As a PhD candidate, you will devise and analyse new methods to make this possible. Join a stimulating research environment, contribute to groundbreaking research and engage with an active academic community!

We are looking for a PhD candidate with a background in numerical analysis to contribute to the development of numerical methods for inverse problems in wave propagation. You will become a member of the Department of Mathematics at Radboud University (Nijmegen, Netherlands), and join the research group of Laura Scarabosio, funded by the NWO Vidi programme ’Taming Frequency in Bayesian Inverse Wave Scattering’.

Inverse wave scattering involves reconstructing the properties of a spatial region from how it interacts with waves. It lies at the core of scientific discovery and technology, from radar to nanoscale metrology and geophysical exploration. The goal of the programme is to develop and rigorously analyse numerical methods for inverse problems in wave scattering within a Bayesian framework. Building on discretisation and solver techniques for time-harmonic wave propagation (governed by the Helmholtz equation) and sampling techniques (such as Monte Carlo methods), we aim to deliver computational tools capable of achieving high‑resolution reconstructions and quantifying their reliability.

As a PhD candidate, you will contribute to the programme’s theme through the design, analysis and implementation of efficient numerical methods. You will also take part in light teaching duties (10%), participate in relevant conferences and workshops and publish your findings in peer‑reviewed journals. The PhD position is funded for four years and will lead to a PhD degree from Radboud University upon successful completion.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

• You hold, or are working towards, a Master's degree in Mathematics or equivalent (to be completed before the start date).
• You have a strong background in numerical analysis and partial differential equations, and you have experience in scientific computing or programming (e.g., Python). Knowledge of uncertainty quantification or inverse problems is welcome but not required.
• You have good verbal and written communication skills.
• You are motivated, proactive and willing to learn (and to struggle!), and you can work both independently and in a team.

• We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4‑year contract).
• You will receive a starting salary of €3,059 gross per month based on a 38‑hour working week, which will increase to €3,881 in the fourth year (salary scale P).
• You will receive an 8% holiday allowance and an 8,3% end‑of‑year bonus.
• We offer Dual Career Coaching. The Dual Career Coaching assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands.
• You will receive extra days off. With full‑time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.

Work and science require good employment practices. Radboud University's primary and secondary employment conditions reflect this. You can make arrangements for the best possible work‑life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself. For example,…