Postdoctoral Researcher — Bayesian Inverse Problems

The University of Oulu; Oulu; On-site; EUR 41,000 - 48,000; Full time

Benefits offered by this job

Wellness benefit ePassi

Buddy Programme

Spouse Network

HR Excellence in Research label

Flexible work-life balance

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

Python

Bayesian methods

Inverse problems

Independent research

Education

Doctoral degree in mathematics or related field

Tools

Git

Job description

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.