奥卢大学PhD position in Computational Uncertainty Quantification, Faculty of Science申请条件要求-申请方
主办方
奥卢大学
PhD直招介绍
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 17,000 people in total. Our northern scientific community operates globally and creates conditions for the emergence of innovations.
We are now looking for
Doctoral Researcher
in Applied Mathematics: Sparse Measurement Strategies for Goal-Oriented Inverse Problems, Faculty of Science, Research Unit of Mathematical Sciences.
The position is part of the SPARSe Academy Fellowship project: “Strategic Planning and Analysis for Reduced Sensing in Inverse Problems” and is connected to the FAME Flagship – Flagship of Advanced Mathematics for Sensing, Imaging and Modelling.
The position is located in the Research Unit of Mathematical Sciences, which has a strong international profile in inverse problems, applied mathematics, computational mathematics, and uncertainty quantification.
About the project
Inverse problems arise in many areas of science and technology, including medical imaging, geophysical imaging, industrial sensing, and astronomical imaging. Traditional approaches usually reconstruct a full image or field first, and then extract the relevant information, such as a tumour boundary, geological interface, or material defect, in a post-processing step.
This project takes a different approach: instead of reconstructing everything, we aim to infer the Quantity of Interest directly from sparse and strategically selected measurements. The central idea is that many relevant quantities in inverse problems have low-dimensional geometric structure, such as curves, surfaces, or interfaces. These can be described using manifold-based models, Bayesian uncertainty quantification, and computational methods for partial differential equations.
The doctoral researcher will contribute to the development of mathematical and computational methods for:
direct inference of low-dimensional Quantities of Interest in inverse problems
uncertainty quantification for manifold-based models
sparse and optimal measurement strategies
numerical algorithms for Bayesian inverse problems
applications in X-ray computed tomography and/or seismic imaging
The project combines applied mathematics, numerical analysis, probability, inverse problems, and computational modelling. It has strong links to real-world applications in medical imaging and geophysical imaging.
Research environment
The doctoral researcher will work under the supervision of Assistant Professor Babak Maboudi Afkham in the Inverse Problems Group at the University of Oulu. The project will be carried out in close connection with the FAME Flagship and the broader Finnish inverse problems research community.
The doctoral researcher will join an active, international, and collaborative research environment with expertise in inverse problems, uncertainty quantification, computational mathematics, mathematical imaging, and applied analysis. The project also includes collaboration opportunities with national and international partners in applied mathematics, medical imaging, and geophysics.
Living and working in Oulu
Oulu is a highly livable city in northern Finland, offering an excellent environment for international researchers. The city combines a strong technology and research ecosystem with a calm, safe, and nature-rich lifestyle.
Oulu offers:
excellent work-life balance
safe and family-friendly living conditions
extensive cycling routes and public sports facilities
easy access to forests, lakes, the sea, and northern nature
opportunities for skiing, hiking, biking, swimming, climbing, and other outdoor activities
a growing international communit
reliable public services, healthcare, and education
For researchers who enjoy both high-level science and outdoor life, Oulu provides an exceptional environment: a compact city with strong infrastructure, short commuting distances, and almost endless possibilities for nature exploration. Read more about Oulu( https://www.oulu.fi/en/university/careers/life-oulu ).
奥卢大学 PhD position in Computational Uncertainty Quantification, Faculty of Science项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
What we offer
We offer a unique opportunity to work on a mathematically rich and application-driven PhD project at the interface of inverse problems, uncertainty quantification, and computational mathematics.
The position offers:
supervision in an internationally active inverse problems research group
a clear three-year PhD project connected to a funded Academy Fellowship and FAME Flagship research environment
opportunities for international collaboration
access to modern computational resources, including national high-performance computing infrastructure
opportunities to contribute to open-source scientific software
a supportive and flexible working culture. Read more about working with us.( https://www.oulu.fi/en/university/careers )
the possibility to develop expertise in both theory and computational applications
Access to sports, culture, and well-being benefits (ePassi). Read more about other staff benefits( https://www.oulu.fi/en/university/careers/staff-benefits ).
Salary
The position is fixed-term for 3 years as of 01.09.2026 or as soon as possible thereafter.
The salary will be based on the levels 2-4 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. 2600-2800 € per month (before taxes). A trial period of 6 months is applied to the position.
奥卢大学Phd申请条件和要求都有哪些？PhD position in Computational Uncertainty Quantification, Faculty of Science项目是不是全奖？有没有奖学金？下面我们一起看一下奥卢大学申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
Who are you?
We are looking for a motivated candidate with a strong background in applied mathematics and an interest in computational inverse problems. A Master’s degree in applied mathematics or a closely related field is required. The master's degree should be awarded before the start of the employment contract.
The ideal candidate has strong knowledge of:
linear algebra
numerical analysis
applied mathematics
scientific computing
A solid understanding of some of the following topics is considered a strong advantage:
partial differential equations
inverse problems
Bayesian methods or uncertainty quantification
probability theory
functional analysis
optimization
finite element methods
spectral methods
numerical methods for PDEs
computational geometry or manifold-based modelling
Programming experience is expected. Experience with Python, Matlab, Julia, or similar scientific computing languages is beneficial.
The applicant does not need to be an expert in all areas listed above. We especially encourage applications from candidates with a strong mathematical foundation, curiosity, independence, and motivation to develop expertise across theory, computation, and applications.
报名方式
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招生人信息
Babak Maboudi Afkham
邮箱：babak.maboudi@oulu.fi
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