Postdoc in Mathematical Analysis & Statistical Learning for Uncertainty Quantification for Inverse Problems

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. 

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 Lyngby Campus.

Application procedure

To apply, please read the full job advertisement, by clicking on the ‘Apply’ button.