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Offer 226 out of 375 from 16/09/24, 13:06

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Freie Uni­ver­si­tät Ber­lin - Fachbereich Mathematik und Informatik - Institut für Mathematik AG Numerical Analysis and Stochastic

SFB/Transregio 388 investigates the interplay between rough analysis and stochastic
dynamics. Central aspects include rough paths and subsequent developments for nonlinear
stochastic partial differential equations. The theory of signatures and rough volatility also
provides important connections to algebra, statistics, and financial mathematics.
Website: https://sites.google.com/view/trr388/
The group “Numerical analysis and stochastic” at Freie Universität Berlin (https://www.mi.fu-
berlin.de/en/math/groups/ag-num-ana-and-stoch/index.html) focuses on analysis and
numerical analysis of (stochastic) partial differential equations (PDEs), especially on
interacting particle systems, surface PDEs and uncertainty quantification. The group
"Numerical analysis of stochastic and deterministic partial differential equations" at Freie
Universität Berlin (https://www.mi.fu-berlin.de/math/groups/naspde) focuses on applied and
computational mathematics, in particular optimization, inverse problems and uncertainty
quantification.

Research assistant (praedoc) (m/f/d)

with 75%part-time job
limited to 30.06.2028 (end of project)
salary grade (Entgeltgruppe) 13 TV-L FU
reference code: AGStoch (Praedoc) 2024-2028 TRR388 (B06)

Working field:

Job description:
The aim of the project B06 of the SFB/TRR 388 is to develop an abstract well-posedness
and regularity theory for (S)PDEs on random time-dependent domains and its numerical
analysis. We will consider quasi-Monte Carlo methods (QMC) for numerical discretization of
quantities of interest in the forward as well as in the (Bayesian) inverse setting. Furthermore,
we will analyze well-posedness of SPDEs on time-dependent domains and study SPDEs on
random timedependent domains. The third-party funded research project provides an
opportunity to do a doctorate.

Requirements:

Requirements:
A completed scientific university degree (Master’s degree) in mathematics until October 2024

Desirable:

  • Very good university degree in mathematics
  • Profound knowledge of stochastic analysis, particularly in SPDEs
  • Strong knowledge of numerical methods for partial differential equations
  • Programming skills in Matlab or Python
  • Profound understanding of Uncertainty Quantification, especially in Bayesian inverse
    problems and Monte Carlo methods
  • Excellent English language skills and good scientific writing and presentation skills

How to apply:

Applications should be sent by e-mail, together with all documents, indicating the reference
code, no later than September 23rd, 2024 in PDF format (preferably as one document) to Frau Prof. Dr. Ana Djurdjevac: adjurdjevac@zedat.fu-berlin.de or postal to

Freie Universität Berlin
Fachbereich Mathematik und Informatik
Institut für Mathematik
AG Numerical Analysis and Stochastic
Prof. Dr. Ana Djurdjevac
Arnimallee 6
14195 Berlin (Dahlem)

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Freie Universität Berlin is an equal opportunity employer.