PhD position in model order reduction and Bayesian inverse problems, Uni Potsdam
The DFG-funded Collaborative Research Center SFB 1294 “Data
Assimilation – The Seamless Integration of Data and Models”, hosted at
the University of Potsdam invites applications for a doctoral
researcher position (75% of full-time employee position with salary
grade TV L - E13) within Project A07: “Model order reduction for
The candidate will work at the Institute of Mathematics at the
University of Potsdam under the supervision of Prof. H. C. Lie. The
candidate will closely collaborate with the group of Prof. M. Freitag
(Institute of Mathematics, University of Potsdam).
Within Project A07, the doctoral researcher will develop the
mathematical theory of Bayesian inference with dimension reduction, in
the context of inverse problems. They will analyse the effect of
low-rank approximations and projection-based dimension reduction on
the posterior measure for infinite-dimensional parameter spaces. They
will also develop and analyse algorithms for Bayesian inference using
model order reduction. The broader aim of this project is to develop
theoretically validated and computationally efficient methods for
Bayesian inference that involve high-dimensional spaces and/or
expensive forward models. Three relevant references are:
1. T. Cui, Y. Marzouk, and K. Willcox. "Scalable posterior
approximations for large-scale Bayesian inverse problems via
likelihood-informed parameter and state reduction". Journal of
Computational Physics, 315:363 – 387, 2016.
2. A. K. Saibaba, J. Chung, and K. Petroske. "Efficient Krylov
subspace methods for uncertainty quantification in large Bayesian
linear inverse problems". Numerical Linear Algebra and Applications,
page e2325, 2020.
3. A. Spantini, A. Solonen, T. Cui, J. Martin, L. Tenorio, and Y.
Marzouk. Öptimal low-rank approximations of Bayesian linear inverse
problems". SIAM Journal of Scientific Computing, 37(6):A2451–A2487,
More information about Project A07 is available at
The SFB 1294 provides an excellent research infrastructure including a
large interdisciplinary network of researchers and its own graduate
school, as well as funding opportunities for conference visits, summer
schools, and hosting international experts.
The SFB 1294 seeks to promote diversity in research, and encourages
qualified applicants of any gender and from any background to apply.
The official announcement is available at
The deadline for the application is the 22nd of September, 2021.