PhD student in Bayesian Computational Mathematics

We invite applications for a fully funded PhD student position to join the research group of Jan Glaubitz to work on Bayesian Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning.

Your workplace

You will be employed at the Division of Applied Mathematics in a welcoming and international work environment. The research group in Computational Mathematics has been one of the most active in Scandinavia over the past decade. Our group is distinguished by its work on fundamental mathematical and computational tools in numerical analysis, computational fluid dynamics, and uncertainty quantification with diverse applications.

Learn more: Department of Mathematics and Work at the Department of Mathematics.

Furthermore, our group maintains active collaborations with other divisions at Linköping University, including the Division of Statistics and Machine Learning (e.g., Fredrik Lindsten and Zheng Zhao), as well as internationally (China, Finland, Germany, South Africa, Spain, Switzerland, USA).  

Your work assignments

A central challenge in computational mathematics, as well as machine learning, is ensuring reliability and trustworthiness. This is especially crucial in applications such as medical diagnosis, weather forecasting, and aircraft design. To improve the reliability and trustworthiness of mathematical models and machine learning tools (e.g., neural networks) in a meaningful way, we need innovative, scalable methodologies that efficiently and accurately capture, represent, and reason about uncertainties within principled frameworks.

You will join the research group of Jan Glaubitz and develop your own research agenda in the context of the group’s research at the intersection of inverse problems, Bayesian learning, and uncertainty quantification. The specific project will be tailored to your expertise and interests; examples include:

1. Efficient inference techniques for high-dimensional Bayesian inverse problems for image reconstruction and chemical reaction neural networks with sparsity-promoting (and edge-preserving) priors, including diffusion-based approaches.
2. Neural solvers for hyperbolic conservation laws and other time-dependent partial differential equations relevant to computational fluid dynamics. These efforts might include Bayesian physics-informed neural networks and neural operators.
3. Bayesian neural networks for approximating piecewise smooth functions and solutions (in imaging and computational fluid dynamics) with uncertainty quantification.

You will have the opportunity to develop, analyze, and implement advanced computational methods, collaborating with leading researchers in Sweden and beyond. Our group maintains active collaborations with other international institutions, such as MIT, Dartmouth College, and Virginia Tech. You will also benefit from professional growth opportunities, including participation in international conferences and publishing in high-impact journals. Moreover, you will have the chance to develop mentoring skills by co-supervising students. You will have access to state-of-the-art computational resources, including the National Supercomputer Centre, to support your research endeavors. Finally, you will receive consistent and structured mentorship to support your career goals.

Applicants with an international background are more than welcome to apply.

As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time.

Your qualifications

You have graduated at Master’s level in Mathematics, Computer/Data Science, Computational Science and Engineering, or a related field, or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses. Alternatively, you have gained essentially corresponding knowledge in another way.

Furthermore, you should be driven and motivated to strive for continuous development. You should also be fluent in English, as you will be actively involved in international collaborations. Finally, you should have some coding experience. 

The employment

When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University

The employment has a duration of normally four years’ full-time equivalent. Extension of employment up to five years is based on the degree of teaching and institutional assignment. Further extensions may be granted in exceptional circumstances. You will initially be employed for one year, after which your employment will be renewed for a maximum of two years at a time, depending on your progress through the study plan. 

Starting date by agreement. 

Salary and employment benefits

The salary of PhD students is determined according to a locally negotiated salary progression.

More information about employment benefits at Linköping University is available here.

Union representatives

Information about union representatives, see Help for applicants.

Application procedure

Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than 2 March, 2026.

Applications and documents received after the date above will not be considered.