Postdoc in Mathematical Analysis and Statistical Learning for Uncertainty Quantification for Inverse Problems at DTU Compute

Do you want to work in an interdisciplinary research team and contribute to the development of and methods for uncertainty quantification (UQ) for inverse problems? We invite applications for a two-year postdoc with focus on the development of mathematical and statistical theory that guides and brings insight to computational methods for UQ.

The position is part of the research initiative CUQI: Computational Uncertainty Quantification for Inverse problems funded by the Villum Foundation and headed by Professor Per Christian Hansen. We consider inverse problems (such as image deblurring, tomographic imaging, source reconstruction, and fault inspection) and we apply methods from Bayesian inference to determine the solution’s sensitivity to errors and inaccuracies in the data, the models, etc. We create a mathematical and computational framework that enables intuitive and extensive application of UQ techniques to a range of inverse problems in academia and industry.

Responsibilities and qualifications

In the Bayesian approach to inverse problems, many different aspects of uncertainty can be handled and quantified. This leads to solutions in terms of probability densities that can be explored theoretically and computationally. In understanding the fundamental properties of solutions, we draw expertise from mathematical and functional analysis, statistical learning theory, and scientific computing, and mix with classic theory for inverse problems. Along this way many new questions concerning consistency and convergence arise, and solutions must be found.This position focuses on the development and use of statistical learning as a framework for formulating and performing computational UQ. You will be responsible for advancing the mathematical and statistical theory behind UQ for inverse problems, e.g., arising from partial differential equations. In addition, you will together with the team aim for bridging the gap between rigorous theoretical analysis and computations.

You will work in a team of PhD students, postdocs, and faculty members in the CUQI project. You are expected to interact with our collaborators on applications of UQ for inverse problems. You will also become part of a department, which plays a key role in education at all levels of the engineering programs offered at DTU, so we are looking for a profile who will also find it exciting to give limited contributions to teaching and training activities as well as supervision of students.

You should have a PhD degree or equivalent in applied mathematical analysis or statistics, scientific computing, computational science, and engineering, applied mathematics, or equivalent academic qualifications. It is an advantage if you can document research in inverse problems or uncertainty quantification. Furthermore, good command of the English language is essential.

If you do not have your PhD diploma at the time of application, please provide a statement from your supervisor.

We offer

DTU is a leading technical university globally recognized for the excellence of its research, education, innovation, and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and terms of employment

The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.The position is a full-time position, and the period of employment is 2 years. We aim for at starting date of 1 May 2023 or as soon as possible after that. The workplace is DTU Compute, Section for Scientific Computing, at DTU Lyngby Campus.You can read more about career paths at DTU here.

Further information

Further information may be obtained from Professor Per Christian Hansen ( and Professor Kim Knudsen ( can read more about DTU Compute at you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.

Application procedure

Your complete online application must be submitted no later than 1 March 2023 (Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply online”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • Application (cover letter)
  • CV
  • Academic Diplomas (MSc/PhD – in English)
  • List of publications
  • Links to other material that may be relevant.

Applications received after the deadline will not be considered.All interested candidates irrespective of age, gender, disability, race, religion, or ethnic background are encouraged to apply.

DTU Compute

DTU Compute is a unique and internationally recognized academic department with 385 employees and 10 research sections spanning the science disciplines mathematics, statistics, and computer science & engineering. We conduct research, teaching, and innovation of high international standard – producing new knowledge and technology-based solutions to societal challenges. We have a long-term involvement in applied and interdisciplinary research, big data and data science, artificial intelligence (AI), internet of things (IoT), smart and secure societies, smart manufacturing, and life science. At DTU Compute we believe in a diverse workplace with a flexible work-life balance.

The Section for Scientific Computing at DTU Compute performs interdisciplinary research in mathematical modeling, numerical analysis and computation methods aimed at complex and large-scale problems in science, engineering and society. It has an internationally recognized team specializing in inverse problems.