PhD position in Bayesian nonparametric statistics, 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 A04: “Nonlinear statistical inverse
problems with random observations”.

The candidate will work at the Institute of Mathematics at the
University of Potsdam under the supervision of Prof. H. C. Lie and
Prof. W. Huisinga. The candidate will collaborate with the group of
Prof. M. Reiss (Institute of Mathematics, Humboldt University Berlin).

Within Project A04, the doctoral researcher will study non-parametric
estimation of covariate effects on the parameters of a time-dependent
process in a Bayesian statistical context. This estimation problem is
motivated by the need for covariate modeling in analyzing clinical
data. The doctoral researcher will develop the mathematical theory of
nonparametric Bayesian inference for nonlinear inverse problems
featuring random design, with a focus on adaptive posterior
concentration and frequentist coverage. One or more examples from
pharmacology will serve as test cases for the developed methods. Three
relevant references are:

1. M. Giordano and R. Nickl. "Consistency of Bayesian inference with
Gaussian process priors in an elliptic inverse problem". Inverse
Problems, 36(8):085001, 2020.

2. J. Rousseau and B. Szabo. Äsymptotic frequentist coverage
properties of Bayesian credible sets for sieve priors". Annals of
Statistics, 48(4):2155–2179, 2020.

3. S. Ghosal and A. van der Vaart. "Fundamentals of nonparametric
Bayesian inference". Cambridge University Press, 2017.

The ideal candidate has mastered measure-theoretic probability,
nonparametric statistics, and Bayesian nonparametric inference, and
has a strong interest in rigorous mathematical statistics and its
applications. The candidate can provide convincing evidence of these
qualifications by coursework, research projects, and/or a master’s
thesis. In addition, the candidate has experience with scientific
computing in R, Matlab, or Python. The candidate has a strong ability
to work effectively, both in collaboration with others and
independently. The candidate must be able to communicate effectively
in both written and spoken English.

More information about Project A04 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.

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