Doctoral Researcher – Computational Uncertainty Quantification

About the role

The University of Oulu seeks a motivated Doctoral Researcher to join the SPARSe Academy Fellowship project on strategic planning and analysis for reduced sensing in inverse problems. The work focuses on developing mathematical and computational methods that directly infer quantities of interest from sparse measurements.

Key responsibilities

• Design and implement algorithms for direct inference of low‑dimensional quantities in inverse problems.
• Develop Bayesian uncertainty quantification techniques for manifold‑based models.
• Create and evaluate sparse and optimal measurement strategies.
• Implement numerical methods for Bayesian inverse problems.
• Apply developed methods to X‑ray computed tomography and/or seismic imaging case studies.

Required profile

• Master’s degree in Applied Mathematics, Computational Science, or a closely related field.
• Strong interest in inverse problems, uncertainty quantification, and numerical analysis.
• Ability to work independently and collaboratively in an international research environment.

Required skills

• Mathematical modeling of inverse problems.
• Bayesian inference and uncertainty quantification.
• Numerical analysis and algorithm development for PDEs.
• Experience with scientific computing (e.g., MATLAB, Python, or similar).

What we offer

• Supervision by Assistant Professor Babak Maboudi Afkham and integration into the Inverse Problems Group.
• Access to a vibrant, interdisciplinary research community and the FAME Flagship.
• Opportunities to work on real‑world applications in medical and geophysical imaging.