Universität Stuttgart
Location
Stuttgart | Germany
Job description
Stellenbeschreibung
The institute of combustion technology for aerospace engineering (IVLR) at the University of Stuttgart is an integrative unit of the DLR institute of combustion technology. The institutes host approximately 80 scientists plus a number of students from various fields of specialization to address research question of modern gas turbines. Thermal conversion of sustainable energy carriers, e.g. green hydrogen, is a key element for the energy transition policy, while synthetic aviation fuels are without alternative for flights beyond mid-range distances. We support this development with the competence fields computer simulation, chemical kinetics and analytics, combustion diagnostics, mass spectrometry, multi-phase flow and high-pressure combustion. We develop and operate various laser diagnostic techniques to resolve the influence of turbulent flow on physical and chemical processes and to analyse the combustion process in detail. Quantitative measurement data form the basis for the development of innovative concepts, as well as for the verification and further development of numerical simulation models. In this context, current development activities should be supported by advanced data analysis. Over the course of time, we have improved our data analysis framework with state-of-the-art methods like ML-based computer vision. We use novel methods for building, training and validating our models. Although, we have a well-established AI framework for object detection, segmentation and statistical analysis we would like to improve it by enabling it to track our objects-of-interest over time. Primarily this should be done to experimental images from Particle Image Velocimetry (PIV) and Shadowgraphy measurements. Once established, the framework can be scaled to other measurement techniques. These serve to further develop numerical models and deepen fundamental understanding of the evolutions of various objects during combustion.
Work Content
Familiarization with computer vision, neural networks for image processing.
Understanding the data analysis pipeline for experimental data.
Evaluation of the general object tracking methods available currently.
Advance object tracking capabilities for scientific images.
Implement the developed solution into the existing framework.
Writing the master's thesis.
Scientific university studies, e.g., in the fields of computer science, physics, chemistry, process engineering, aerospace engineering, or mechanical engineering.
Strong programming skills, preferably in Python.
Experience in the field of computer vision and machine learning, enthusiasm for programming.
Fundamental understanding of combustion processes and spectroscopy is advantageous.
Teamwork, dedication, ability to work independently, and a scientific approach.
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