Research associate / Doctoral researcher position in Bayesian nonparametric statistics

Universität/Firma :Universität Potsdam
Fachgebiet :Mathematics
Kontakt :hanlie@uni-potsdam.de
Webadresse :https://www.sfb1294.de/fileadmin/user_upload/Open_positions/A04_2ndround.pdf

Ausschreibungstext:

The DFG-funded Collaborative Research Center SFB 1294 “Data Assimilation – The Seamless Integration of Data and Models”, hosted at the University of Potsdam jointly with its partner institutions HU Berlin, TU Berlin, WIAS Berlin and GFZ 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.

More information about Project A04 is available at https://www.sfb1294.de/research/research-area-a/a04 .

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.

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.

Applications to the SFB should be submitted via https://www.geo-x.net/sfb-1294/ and should include the following in a single PDF file:

    A statement of research interests and motivation;
    A full CV;
    The names, e-mail addresses and/or reference letters of at least two referees;
    Academic transcripts;
    A link to an electronic version of your Master/Diploma thesis; and
    A list of publications/talks/presentations.

In your application, please indicate that you are applying for the doctoral researcher position in project A04, and explain your motivation for applying to this position.

Diese Anzeige wurde am 03/09/2021 um 11:14 übermittelt und verfällt am 01/10/2021.

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