Postdoctoral Researcher, Mathematical Sciences, Faculty of Science
The University of Oulu
A complete application in a minute — tailored resume and cover letter, ready to send.
Benefits offered by this job
Job summary
The University of Oulu invites applications for a Postdoctoral Researcher in the Research Group on Computational Uncertainty Quantification. The role focuses on Bayesian inverse problems, uncertainty quantification, and development of priors with close ties to SPARSe Academy and the FAME Flagship.
You will conduct independent and collaborative research, develop computational methods, and contribute to teaching and supervision while engaging with national and international collaborators.
Qualifications
- PhD in mathematics, applied mathematics, or closely related field with strong analytical background.
- Proven ability to conduct independent scientific research and collaborate internationally.
- Fluent written and spoken English; teaching experience is a plus.
Responsibilities
- Conduct independent and collaborative research in Bayesian inverse problems and uncertainty quantification.
- Develop and analyze new Bayesian priors and computational methods for high/infinite dimensional posteriors.
- Develop research software in Python and contribute to open research codebases.
- Lead supervision of undergraduate and graduate students and contribute to teaching.
- Collaborate with international partners and industrial collaborators within FAME and SPARSe projects.
Skills
Education
Tools
Job description
The University of Oulu is a multidisciplinary, international research university, with about 4000 employees who produce new knowledge based on high-standards research and provide research based education to build a more sustainable, smarter, and more humane world. The University of Oulu community has about 19,000 people in total. Our northern scientific community operates globally and creates conditions for the emergence of innovations. The University of Oulu is ranked in the top 3% of the world’s universities. All our research, education, and community activities are guided by our university’s values and ethical principles.
We are now looking for
to join us in the Research Group on Computational Uncertainty Quantification at the Faculty of Science, University of Oulu.
The Research Group on Computational Uncertainty Quantification develops mathematical and computational methods for quantifying uncertainty in inverse problems. Our research combines rigorous mathematical analysis, Bayesian methods, and scalable computational approaches, with applications ranging from imaging and sensing to industrial inverse problems. The group is part of the Mathematical Sciences Research Unit and works closely with the Inverse Problems research community at the University of Oulu, as well as national and international collaborators.
The position focuses on developing new mathematical and computational methods for Bayesian inverse problems and uncertainty quantification. A particular emphasis will be placed on the design and analysis of new Bayesian prior models for inverse problems and on developing methods for exploring complex high-dimensional and potentially infinite-dimensional posterior distributions.
As a postdoctoral researcher, you will:
- conduct independent and collaborative research in applied mathematics, with a particular focus on Bayesian inverse problems and computational uncertainty quantification.
- develop and analyse new classes of Bayesian priors for inverse problems, including their mathematical properties and computational implementation.
- develop new mathematical and computational methods for exploring high-dimensional and potentially infinite-dimensional posterior distributions.
- contribute to the development and implementation of research software, particularly in Python, for computational uncertainty quantification and inverse problems.
- conduct research in connection with the SPARSe Academy Research Fellowship project and the Finnish Centre of Excellence in Randomness and Structures (FAME) Flagship, and contribute to ongoing international collaborations, including collaboration with researchers at Johannes Kepler University Linz and industrial partners. These collaborations aim, among other objectives, to develop mathematical and computational uncertainty quantification methods that can be made accessible and useful for industrial applications.
- contribute to the supervision of undergraduate and graduate students and participate in the research activities of the group.
- contribute to teaching in mathematics and applied mathematics according to the needs of the research unit.
- be part of the Research Group on Computational Uncertainty Quantification, led by Assistant Professor Babak Maboudi Afkham, and interact closely with the broader inverse problems research community at the University of Oulu.
- be part of the FAME Flagship on a broader scale and have opportunities to interact with its national and international network of researchers and collaborators.
- The support of an experienced and enthusiastic research team, with whom you can share your expertise and grow as an expert.
- Development and career options of the big organization.
- We have an HR Excellence in Research -quality label, which is a recognition awarded by the European Commission for the development of researchers’ working conditions and careers.
- Finland is one of the most livable countries in the world, with a high quality of life, safety, an excellent education system, and a competitive economy. Read more about living in Oulu .
In order to succeed and enjoy the position, we require from you:
- Doctoral degree in mathematics, applied mathematics, or a closely related field, with a strong mathematical background, particularly in analysis. The doctoral degree must have been completed by the closing date for applications. The degree must have been obtained no more than 10 years ago.
- Ability to engage in independent scientific research.
- Teaching skills required for the position.
- Strong background in mathematical analysis and the mathematical foundations relevant to inverse problems and uncertainty quantification.
- Strong software development and scientific computing skills, particularly in Python.
- Ability and motivation to conduct both theoretical and computational research.
- Fluent written and spoken English.
- Ability to work effectively both independently and as part of an interdisciplinary and international research environment.
We also appreciate:
- A strong background in pseudodifferential operator theory.
- Previous research experience in inverse problems, Bayesian inverse problems, uncertainty quantification, or closely related areas.
- Experience with high-dimensional or infinite-dimensional Bayesian inference.
- Experience in developing computational methods for inverse problems or statistical inference.
- Experience in supervising students or contributing to university-level teaching.
- Experience with collaborative research involving researchers from different disciplines or industrial partners.
When assessing the candidate’s merits for the position of Postdoctoral Researcher, attention will be paid to scientific excellence, such as research outputs, international scientific activities and collaboration, supervision of undergraduate students, pedagogical competence, societal relevance, impact and potential of scientific activities, and services for the academic community.
We are now looking for
to join us in the Research Group on Computational Uncertainty Quantification at the Faculty of Science, University of Oulu.
The Research Group on Computational Uncertainty Quantification develops mathematical and computational methods for quantifying uncertainty in inverse problems. Our research combines rigorous mathematical analysis, Bayesian methods, and scalable computational approaches, with applications ranging from imaging and sensing to industrial inverse problems. The group is part of the Mathematical Sciences Research Unit and works closely with the Inverse Problems research community at the University of Oulu, as well as national and international collaborators.
The position focuses on developing new mathematical and computational methods for Bayesian inverse problems and uncertainty quantification. A particular emphasis will be placed on the design and analysis of new Bayesian prior models for inverse problems and on developing methods for exploring complex high-dimensional and potentially infinite-dimensional posterior distributions.
As a postdoctoral researcher, you will:
- conduct independent and collaborative research in applied mathematics, with a particular focus on Bayesian inverse problems and computational uncertainty quantification.
- develop and analyse new classes of Bayesian priors for inverse problems, including their mathematical properties and computational implementation.
- develop new mathematical and computational methods for exploring high-dimensional and potentially infinite-dimensional posterior distributions.
- contribute to the development and implementation of research software, particularly in Python, for computational uncertainty quantification and inverse problems.
- conduct research in connection with the SPARSe Academy Research Fellowship project and the Finnish Centre of Excellence in Randomness and Structures (FAME) Flagship, and contribute to ongoing international collaborations, including collaboration with researchers at Johannes Kepler University Linz and industrial partners. These collaborations aim, among other objectives, to develop mathematical and computational uncertainty quantification methods that can be made accessible and useful for industrial applications.
- contribute to the supervision of undergraduate and graduate students and participate in the research activities of the group.
- contribute to teaching in mathematics and applied mathematics according to the needs of the research unit.
- be part of the Research Group on Computational Uncertainty Quantification, led by Assistant Professor Babak Maboudi Afkham, and interact closely with the broader inverse problems research community at the University of Oulu.
- be part of the FAME Flagship on a broader scale and have opportunities to interact with its national and international network of researchers and collaborators.
- The support of an experienced and enthusiastic research team, with whom you can share your expertise and grow as an expert.
- Wellness benefit ePassi covering sport, culture, and well-being. Read more about other staff benefits here.
- Our Buddy Programme and Spouse Network support you and your close ones in settling into Oulu.
- Development and career options of the big organization.
- We have an HR Excellence in Research -quality label, which is a recognition awarded by the European Commission for the development of researchers’ working conditions and careers.
- Work that matters and a workplace that promotes flexibility and work-life balance. Read more about working with us.
- Finland is one of the most livable countries in the world, with a high quality of life, safety, an excellent education system, and a competitive economy. Read more about living in Oulu .
In order to succeed and enjoy the position, we require from you:
- Doctoral degree in mathematics, applied mathematics, or a closely related field, with a strong mathematical background, particularly in analysis. The doctoral degree must have been completed by the closing date for applications. The degree must have been obtained no more than 10 years ago.
- Ability to engage in independent scientific research.
- Teaching skills required for the position.
- Strong background in mathematical analysis and the mathematical foundations relevant to inverse problems and uncertainty quantification.
- Strong software development and scientific computing skills, particularly in Python.
- Ability and motivation to conduct both theoretical and computational research.
- Fluent written and spoken English.
- Ability to work effectively both independently and as part of an interdisciplinary and international research environment.
We also appreciate:
- A strong background in pseudodifferential operator theory.
- Previous research experience in inverse problems, Bayesian inverse problems, uncertainty quantification, or closely related areas.
- Experience with high-dimensional or infinite-dimensional Bayesian inference.
- Experience in developing computational methods for inverse problems or statistical inference.
- Experience in supervising students or contributing to university-level teaching.
- Experience with collaborative research involving researchers from different disciplines or industrial partners.
- Finnish language skills.
When assessing the candidate’s merits for the position of Postdoctoral Researcher, attention will be paid to scientific excellence, such as research outputs, international scientific activities and collaboration, supervision of undergraduate students, pedagogical competence, societal relevance, impact and potential of scientific activities, and services for the academic community.
The salary will be based on the levels 5-6 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. 3700-4300 € per month (before taxes).
A trial period of 6 months is applied to the position.
If you have any further questions, please contact Assistant Professor Babak Maboudi Afkham, Faculty of Science, Mathematical Sciences Research Unit, University of Oulu, babak.maboudi@oulu.fi
'/%3e%3cpath%20d='M101.744%2012.4726L102.894%2016.1947L99.0021%2013.2201L101.744%2012.4726ZM105.3%208.40937H100.488L99.0026%203.59473L97.5116%208.41047L92.7%208.40385L96.5965%2011.3834L95.1055%2016.1942L99.0021%2013.2196L101.408%2011.3834L105.3%208.40992V8.40937Z'%20fill='white'/%3e%3cdefs%3e%3clinearGradient%20id='paint0_linear_5593_1381'%20x1='90'%20y1='9.44471'%20x2='99'%20y2='9.44471'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%2300AD70'/%3e%3cstop%20offset='0.998594'%20stop-color='%2300B67A'/%3e%3cstop%20offset='1'%20stop-color='%23DCDCE6'/%3e%3c/linearGradient%3e%3c/defs%3e%3c/svg%3e){kind=small}
