DOCTORAL RESEARCHER IN "Bayesian treatment of model inexactness in dynamic inverse problems"

Position-ID:1799Faculty/ Facility:Mathematics and Physics Institute/ Facility:Mathematics and Physics : IMNG - Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling  Research Association:Data-Integrated Simulation Science (SimTech) Teaching Obligation:No Application deadline:12/08/2025Anticipated Start Date: 01/01/2026   About Us   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 way. In 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-Rigaud's working group "Optimization and inverse Problems" at the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling (IMNG).     Your Tasks  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 partnersPublication of research resultsActive participation in SimTech events (Status seminars, Project Network meetings, furtherevents)     Your Profile  Very good Master's degree or PhD in mathematics, natural sciences, engineering or a related field, ideally having written the master's or doctoral thesis in the area of applied mathematicsYou have thorough knowledge in one or more of the following mathematical subjects:Inverse problemsBayesian statistics  Stochastic processes  Uncertainty quantification  Experience with programming languages and frameworks for Bayesian modeling and uncertainty quantification, e.g. Python or C++/JuliaYou are motivated to work in an interdisciplinary project teamProficiency in English is required, knowledge in German is welcome but not compulsoryWe search for an open-minded person with good communication skills    Your Benefits  An inspirational and supportive research environment at the Cluster of Excellence SimTech with ample networking opportunitiesA nationally and internationally well-connected research groupFully funded conference visits and a fully funded research stay abroadDiverse and responsible tasks in a growing interdisciplinary and intercultural teamYou will be part of the SimTech Graduate SchoolTraining 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 Employment and compensation information   Maximal Funding Period or Duration of Employment: temporary until 31/12/2027  Type of Funding: Position as Employee at the University of Stuttgart Compensation: EG TV-L 13   Percentage of weekly working hours (usually 39.5h = 100%): 100%  Employment at the cooperation partner: No Location: Stuttgart, Campus Vaihingen If Location other than Stuttgart or additional location(s): N/A   ​Contact Details   Contact person: Prof. Bernadette Hahn-Rigaud Mail:  Phone: Website: " target="_blank">    [Not translated in selected language]