(POST-) DOCTORAL RESEARCHER IN "Bayesian treatment of model inexactness in dynamic inverse problems"

The Stuttgart Center for Simulation Science (SC SimTech) constitute a long-standing prime example of establishing and structurally supporting interdisciplinary research. SC SimTech serves as the institutional backbone of the EXC 2075. The SC SimTech including the EXC 2075 is an interdisciplinary research center with more than 200 scientists of different ages gender identities nationalities and different subject areas jointly performing research towards a common goal: We target a new class of modeling and computational methods based on available data from various sources in order to take the usability precision and reliability of simulations to a new level.

The project & your career perspective:

This position is funded by the Cluster of Excellence EXC 2075 and is part of the Project Network Bayesian Inference for Stochastic Models: Generalized Methods for Forward and Backward Uncertainty Quantification (Bayes 2.0).

Imaging modalities such as computerized tomography (CT) are concerned with recovering information about the interior structure of a studied object in a non-invasive many medical or industrial applications the quantity of interest changes during the data acquisition. Thus the actual image reconstruction step requires suitable a priori information on the dynamics. This can be incorporated explicitly e.g. via diffeomorphic motion models or implicitly by interpreting motion as an inexactness in the forward model. Conventional approaches treat such model deviations deterministically ignoring their stochastic nature and hindering uncertainty quantification.

This project develops a Bayesian framework for inverse problems with inexact forward operators. Model inexactness will be interpreted first as a random variable and then as a stochastic process to capture time-dependent deviations. The main goal of the project is to explore suitable priors for both the unknown and the model error and to develop computationally feasible approaches for posterior exploration.

You will be a member of Prof. Bernadette Hahn-Rigauds working group Optimization and inverse Problems at the Institute of Mathematical Methods in Engineering Numerical Analysis and Geometric Modeling (IMNG).

• Stochastic modeling of model inexactness
• Derivation of priors for unknown parameters and model error e.g. based on physical constraints
• Development of efficient posterior exploration methods and dimension reduction techniques e.g. via likelihood-informed subspaces
• Close collaboration with our network partners
• Publication of research results
• Active participation in SimTech events (Status seminars Project Network meetings further
  events)

• Very good Masters degree or PhD in mathematics natural sciences engineering or a related field ideally having written the masters or doctoral thesis in the area of applied mathematics
• You have thorough knowledge in one or more of the following mathematical subjects:
  • Inverse problems
  • Bayesian statistics
  • Stochastic processes
  • Uncertainty quantification
• Experience with programming languages and frameworks for Bayesian modeling and uncertainty quantification e.g. Python or C/Julia
• You are motivated to work in an interdisciplinary project team
• Proficiency in English is required knowledge in German is welcome but not compulsory

We search for an open-minded person with good communication skills

• An inspirational and supportive research environment at the Cluster of Excellence SimTech with ample networking opportunities
• A nationally and internationally well-connected research group
• Fully funded conference visits and a fully funded research stay abroad
• Diverse and responsible tasks in a growing interdisciplinary and intercultural team
• You will be part of the SimTech Graduate School
• Training programs to support your first steps as an early career scientist
  
  

Application procedure:

Please apply via the career portal of the University of Stuttgart and submit your complete application including one-page motivation letter academic CV one letter of reference as well as academic certificates until December 8th 2025. The starting date is negotiable. If you have any questions regarding this application please contact us via .