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This project focuses on developing advanced methods for uncertainty quantification in inverse problems, i.e., mathematical problems where we aim to determine unknown causes based on indirect or incomplete measurements. Such problems are inherently challenging because small errors in data can lead to large uncertainties in the solution. Accurately quantifying and managing these uncertainties is a key frontier in applied mathematics.

A central goal of the project is to not only understand these uncertainties but also to reduce them. This is done through goal-oriented approaches that identify and prioritize the most informative measurements, i.e., those that provide the most insight into the specific aspects of the unknowns that are of practical interest.

The postdoctoral researcher will contribute to the theoretical foundations of inverse problems involving wave phenomena, develop cutting-edge computational algorithms, and apply these techniques to real-world problems, particularly in wave-based imaging and medical applications.

This position is part of the Finnish Inverse Problems Society and the FAME Flagship initiative, offering excellent opportunities to collaborate with leading mathematicians and physicists in Finland and worldwide. In addition, the position is linked to the DREAM doctoral pilot program, which provides the candidate with valuable experience in the supervision of doctoral students. Teaching responsibilities will be limited, with a maximum of 20% of total working time.

FAME Flagship and DREAM pilot: f fameflagship.fi/; FIPS: f ips.fi/

The project will be carried out within the Inverse Problems Group, a multidisciplinary research team in the Research Unit of Mathematical Sciences. The group brings together expertise in applied mathematics, computational methods, and imaging sciences, and actively collaborates with leading international researchers. The position will be supervised by Assistant Professor Babak Maboudi Afkham and Associate Professor Andreas Hauptmann.

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To succeed and enjoy the position, you have

• A strong background in inverse problems and their computational solution.
• Experience with uncertainty quantification is highly desirable.
• Excellent programming skills in Python are essential; familiarity with C++ is considered an asset.
• Solid understanding of mathematical modeling, with partial differential equations (PDEs) and their numerical solution.
• Ability to work both independently and as part of a collaborative, international research team.

Further requirements are

• Doctoral degree in applied mathematics, or related fields. The degree must have been obtained no more than 10 years ago.
• Teaching skills

The position is fixed-term for 2 years as of 1.12.2025 or as soon as possible thereafter.

The salary will be based on level 5 of the demand level chart for teaching and research staff of Finnish universities. In addition, a salary component based on personal work performance will be paid (a maximum of 50 % of the job-specific component). The starting gross salary will be approx. 3800-4000 € per month (before taxes).

A trial period of 6 months is applied to the position.

Interested? If yes, please apply by 6.10.2025 (23:59 Finnish local time) through our recruitment system.

The application should be written in English and include the following:

1. CV, following the guidelines of the Finnish Advisory Board on Research Integrity
2. Cover letter
3. List of publications, following the guidelines of the Academy of Finland
4. Certificates/Diplomas
5. Contact information of 2 persons available for recommendation

The eligible applicants fitting best in the profile expected for the position will be invited to an on-site or remote interview. All applicants will be notified during the selection process.

We welcome applicants from all backgrounds, such as people of different ages, different genders, and members of different languages, cultural or minority groups.

For further information, please contact Assistant Professor Babak Maboudi Afkham (Research Unit of Mathematical Sciences, Faculty of Science) by email at babak.maboudi@oulu.fi.