Research Associate for The Project “dfg Research
vor 2 Monaten
Research Associate for the Project “DFG Research Training Group: Modelling, Simulation and Optimization of Fluid Dynamic Applications” - 28 Subsection 3 HmbHG
- InstitutionFaculty of Mathematics, Informatics and Natural Sciences, Department of Mathematics
Salary levelEGR. 13 TV-L
Start dateas soon as possible or 01.11.2023, fixed until 31.10.2026 (This is a fixed-term contract in accordance with Section 2 of the academic fixed-term labor contract act [Wissenschaftszeitvertragsgesetz, WissZeitVG]).
Application deadline30.09.2023
Scope of workpart-time
Weekly hours75 % of standard work hours per week**Responsibilities**:
Duties include academic services in the project named above. Research associates may also pursue independent research and further academic qualifications.
Specific Duties
PhD project O1.2 at Universität Hamburg:
- Convex Integration
PhD project O2.2 at Technische Universität Hamburg (TU Hamburg):
- efficient model-order reducing approaches to unsteady optimization
- optimization of unsteady propeller-hull-interaction
- robust & risk aware transient optimization. Examples included should address shape optimization of energy saving devices for modern propulsion systems.
(1) Application of quantum-inspired tensor network methods to formulate reduced-order models for wall-bounded flows. (2) Formulation of classical Finite Volume building blocks using Matrix Product State and Matrix Product Operator formats. (3) Transfer of Tensor Network simulations to Quantum gate level simulations using variational quantum methods. Examples included should address laminar/turbulent attached and separated wall-bounded engineering shear flows.
PhD project O4 at Universität Hamburg:
- Coupling finite elements and data driven models in identification/learning of flow laws
Aims & Motiviation:
In the identification/learning of governing equations of a fluid, unknown parameters and material laws (such as the stress-strain relation) need to be identified from measurements by minimizing the mismatch/loss between (numerical) model predictions and measurement data. In some situations, these unknown parameters may be just a finite set of real numbers.
However, when coupling with different physical quantities such as temperature, or polymer densities, the material laws may also be unknown functions which need to be identified. Due to their known approximation quality neuronal networks (NN) may be used to approximate these unknown functions for flow in porous media, but fitting to preselected sets of functions is also possible. Similar ideas have also been used for improved turbulence modeling. In order to simulate the flow based on such a data driven/empirical flow law the governing partial differential equation (PDE) needs to be discretized, e.g., by finite elements.
When training the NN the accuracy of the discretization needs to be carefully tuned to avoid excessive training cost and potential stalling of the training.
Apprach & Innovation:
In contrast to so called physics informed NN where the unknown solution of the PDE is approximated by a NN, we want to approximate the unknown material laws by a NN while continuing to use standard approaches for the discretization.
In this context the possible PhD topics will deal with the inherent difficulties associated with the coupling of a finite element discretization of the PDE with a NN for the material laws. This includes the efficient adjoint based gradient calculation for the coupled problem, the error assessment of the coupled system for the prediction and identification problem by means of adloint baes error estimation as well as the analysis and design of suitable optimization methods for the training of the material law NN.
**Requirements**:
A university degree in a relevant field.
We offer
- Reliable remuneration based on wage agreements
- Continuing education opportunities
- University pensions
- Attractive location
- Flexible working hours
- Work-life balance opportunities
- Health management
- Educational leave
- 30 days of vacation per annum
As a University of Excellence, Universität Hamburg is one of the strongest research universities in Germany. As a flagship university in the greater Hamburg region, it nurtures innovative, cooperative contacts to partners within and outside academia. It also provides and promotes sustainable education, knowledge, and knowledge exchange locally, nationally, and internationally.
Severely disabled and disabled applicants with the same status will receive preference over equally qualified non-disabled applicants.
Instructions for applying
Contact
Prof. Dr. Armin Iske
+49 40 42838-5264
Prof. Dr. Thomas Rung
+49 40 42878-6054
Reference number
- 335Location
Bundesstraße 55
20146 Hamburg
Application deadline
- 30.09.2023- a cover letter (no more than two pages)
- a tabular curriculum vitae
- a list of publications
- a list of conference attendances
- transcripts of records for their Bachelor and
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