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The consortium of the Transregio-CRC 154 is seeking highly motivated and qualified students that are looking for obtaining a doctoral degree in applied mathematics. Once recruited, you will perform research in a highly relevant research area in applied mathematics that is centered around the “turnaround in energy policy”, in particular in the context of gas networks. The main aim of the Transregio-CRC is to provide certified novel answers to mathematical challenges arising in this context, based on mathematical modeling, simulation, and optimization. In order to achieve these goals new paradigms in the integration of these disciplines and, in particular, in the interplay between integer and nonlinear programming in the context of stochastic data have to be established and brought to bear.
The CRC 154 is financed by the German Science Foundation, the third funding phase lasts from July 1 st , 2022, until June 30 th , 2026. The CRC 154 project descriptions and other details are contained in the corresponding attachment. The call for applications is open until further notice. There is no fixed deadline for an application, but positions will be offered to suitable candidates on a first-come first-serve basis. We especially encourage applications by female candidates.
You are eligible to apply for a position within the CRC 154, if
Once recruited, we will offer:
With a single application, you may apply for more than one position within the CRC 154 (maximum 10), in order of preference.
You will need to provide us with the following documents:
a) Application form (see here)
b) Letter of motivation (max. 1 page)
c) Copies of degree and academic transcripts (with grades and rankings)
d) Brief summary of Master's thesis (max. 1 page)
e) Short CV including letter/s of recommendation and publication list (if any)
All the above-mentioned documents must be collected in a single pdf file and have to be uploaded on EasyChair on
https://easychair.org/conferences/?conf=trr1543
after creating an account on easychair.org.
Please include your data for “author 1” and tick the “corresponding author” box.
As title and as abstract, please choose “Application for CRC 154”.
As keywords, please give the same ranking of the CRC 154 subprojects you apply to as you have given in the application form.
We will only consider applications if they are uploaded there.
We will come back to you soon. Shortlisted candidates will be invited for an interview (traveling to each partner's site may not be necessary). Winners will be announced as soon as possible. Applications will be considered till the corresponding positions have been filled.
Alexander Martin (FAU), Sebastian Pokutta (TU Berlin), position to be filled at TU Berlin
In this subproject we study domain decomposition approaches for optimal control problems using the example of gas transport networks. Our main goal is to couple the space-time-domain decomposition method from the second phase with machine learning and mixed-integer programming techniques. To this end, we develop an interlinked data-driven and physics informed algorithm called NeTI (Network Tearing and Interconnection) that combines mixed-integer nonlinear programming, learning of surrogate models, and graph decomposition strategies.
Max Klimm (TU Berlin), Marc Pfetsch (TU Darmstadt), Martin Skutella (TU Berlin), position to be filled at TU Berlin
The goal of this project is to provide a comprehensive study of the problem of maximizing the welfare in hydrogen or gas network. Welfare is generated by satisfying routing requests where the ability to satisfy requests is limited by the physical capacity of the networks, e.g., in terms of bounds on the maximum pressures at the nodes. These questions are formulated as non-linear packing problems that lead to a coupling of the discrete decision whose requests have to be satisfied and fractional decisions about how the flows are distributed over time and within the network. The project further studies variants of the problem where the requests appear in random order, and/or a mechanism design variant where the welfare of satisfying a request is unknown to the network operator.
Jens Lang (TU Darmstadt)
The goal of this project is to develop a holistic dynamic multi-scale ansatz for the numerical solution of compressible instationary Euler equations with uncertain data on network structures. We will use these methods for uncertainty quantification and adaptive multi-level probabilistic constrained optimization on flow networks. For this, we combine adaptive stochastic collocation methods with kernel density estimators in an adjoint-based gradient method.
Caroline Geiersbach, Michael Hintermüller (WIAS Berlin)
This subproject is concerned with the coupling of an intraday gas market with the physical transport of gas through a network, subject to uncertainty. This problem is modelled as a non-cooperative equilibrium problem where each risk-averse market player makes decisions in such a way as to maximize profit while simultaneously ensuring that their collective decisions are physically feasible along the network. The goal of this project is to characterize and compute equilibria to this problem. For this, we study the existence of solutions and their sensitivity to perturbations in parameters. To develop algorithms to handle the problem computationally, stochastic approximation and feedback-type mechanisms are employed.
Frauke Liers (FAU Erlangen-Nürnberg), Andrea Walther (HU Berlin), position at HU Berlin
Bilevel optimization is a wide area in mathematical optimization and plays an important role in the TRR 154, where the coupling of producers and consumers represents one prominent application and the robust protection against uncertainties another one. Many of these bilevel problems can be formulated as nonsmooth, piecewise linear optimization problems with constraints. This project aims at the development, analysis and implementation of a structure-exploiting algorithm for bilevel problems of this kind building on the quite recent approach of abs-linearization and the active signature method. This allows also to treat nonsmooth functions in a bilevel optimization.
Michael Hintermüller (WIAS Berlin)
The goal of this project is the development of stochastic gradient methods for the treatment of almost sure state constraints. Such constraints arise for example in the nomination validation of gas networks under uncertain demands but also play a role in the transition towards future hydrogen networks. A focus of the project is the consideration of sequences of relaxed problems intertwined with the stochastic gradient method and a rigorous mathematical convergence analysis of the resulting methods.
