Doctoral Researcher in Computational Uncertainty Quantification

About the role

The University of Oulu invites applications for a Doctoral Researcher position in Computational Uncertainty Quantification within the Faculty of Science. The role focuses on developing sparse measurement strategies for goal‑oriented inverse problems, aiming to infer quantities of interest directly from limited data.

Key responsibilities

• Design and analyse mathematical models for direct inference of low‑dimensional quantities of interest.
• Develop Bayesian uncertainty‑quantification frameworks for manifold‑based models.
• Create and test sparse, optimal measurement strategies.
• Implement numerical algorithms for Bayesian inverse problems.
• Apply methods to real‑world cases such as X‑ray computed tomography and seismic imaging.

Required profile

• Master’s degree in Applied Mathematics, Computational Science, or a closely related field.
• Strong background in inverse problems and numerical analysis.
• Interest in interdisciplinary research linking mathematics, probability, and imaging.
• Ability to work in an international, collaborative research environment.

Required skills

• Applied mathematics
• Computational mathematics
• Uncertainty quantification
• Inverse problems
• Partial differential equations
• Bayesian inference
• Numerical analysis

What we offer

• PhD position within the SPARSe Academy Fellowship project.
• Access to the FAME Flagship and a vibrant Finnish inverse‑problems community.
• Funding for research activities and participation in international conferences.