PhD Position in Inverse Problems for Wave-Based Imaging
Develop novel mathematical and computational frameworks for robust, efficient wave-based imaging via PDE-constrained inverse problems.
Best for:Best for mathematicians or applied scientists with a strong background in PDEs, inverse problems, and numerical methods, seeking to bridge theory and computational imaging.
Overview
This PhD position at Centrum Wiskunde & Informatica (CWI) focuses on advancing inverse problems for wave-based imaging, a critical area spanning geophysics, medical ultrasound, and non-destructive testing. The project aims to combine PDE-based modeling with data-driven reduced-order techniques to create computationally efficient and robust imaging methods. The successful candidate will conduct rigorous mathematical analysis of nonlinear inverse problems, design numerical algorithms, and implement prototypes for real-world applications. The research bridges theoretical rigor and practical computational challenges, with opportunities to collaborate across disciplines and industries. Supervision will be provided by Prof. Tristan van Leeuwen and Dr. Chris Stolk, ensuring a dynamic and interdisciplinary environment.
What you'll do
- Develop mathematical theory for nonlinear inverse problems governed by wave equations.
- Design and analyze numerical algorithms for inverse problems, uncertainty quantification, and experimental design in wave-based imaging.
- Implement prototype numerical methods and test them on increasingly complex computational wave-based imaging problems.
- Collaborate with researchers in applied mathematics, scientific computing, and wave-based imaging, as well as academic and industrial partners.
- Present research findings at international conferences and publish in leading scientific journals.
Requirements
Degree required: Master's degree in mathematics, applied mathematics, computational science, mathematical physics, or a closely related field
Degree offered: PhD
Language: English; proficiency required for academic work and collaboration
Instruction: English
- Strong background in partial differential equations (PDEs)
- Experience with inverse problems and numerical methods for PDEs
- Ability to work with rigorous mathematical arguments and numerical experiments
- Programming proficiency in Python, Julia, MATLAB, C, or C++
- Excellent written and spoken English
- Capacity for independent research and interdisciplinary collaboration
- No specific nationality or residency restrictions mentioned
- Open to all qualified candidates
Funding & terms
How to apply
Deadline: 2026-10-31
Submit a motivation letter, CV with two reference email addresses, transcripts of Bachelor’s and Master’s programmes, a copy of the Master’s thesis (or draft), and any relevant publications or scientific work. Applications must be sent by the deadline.
- Motivation letter
- CV including two reference email addresses
- Transcript of grades from Bachelor’s and Master’s programmes
- Copy of Master’s thesis or draft
- List of publications or other relevant scientific work (if applicable)
Contact
Prof. Tristan van Leeuwen· Supervisor
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