Master Students Aerospace, Mechanical Engineering, Computer Science, Mathematics or similar

vor 4 Wochen

Aachen, Deutschland Deutsches Zentrum für Luft- und Raumfahrt (DLR) Vollzeit

Steigen Sie ein in die faszinierende Welt des Deutschen Zentrums für Luft- und Raumfahrt (DLR), um mit Forschung und Innovation die Zukunft mitzugestalten Mit dem Know-how und der Neugier unserer 11.000 Mitarbeitenden aus 100 Nationen sowie unserer einzigartigen Infrastruktur, bieten wir ein spannendes und inspirierendes Arbeitsumfeld. Gemeinsam entwickeln wir nachhaltige Technologien und tragen so zur Lösung globaler Herausforderungen bei. Möchten Sie diese große Zukunftsaufgabe mit uns zusammen angehen? Dann ist Ihr Platz bei uns

For our Institute of Structures and Design in Aachen-Merzbrück (Würselen) we are looking for Master Students Aerospace, Mechanical Engineering, Computer Science, Mathematics or similar (f/m/x), Development of data augmentation techniques for timeseries data measured from mechanical testing

Das erwartet Sie:

In the DLR Project “Kennwertdatenbank und optimierte Produktionstechnik für Kleinflugzeuge” (KoPf), we aim to accelerate material testing using machine learning models by identifying patterns and relationships from the material testing, which can lead to the development of a composite structure with a target structural performance. This goal can be achieved through the reshaping of the testing pyramid (i.e. building block approach) to correlate the behaviour at larger scaled structures to a small number of fundamental material properties by analysing data on existing materials.

The size and consistency of timeseries datasets are crucial to accurately analyse and predict the material behaviours with data-driven approach. However, acquiring such data through physical experiments alone is often limited by constraints such as cost and time. To overcome such limitations, there is a growing interest in leveraging data augmentation techniques to generate synthetic timeseries data that complements existing experimental datasets.

The primary objective of this study is to investigate data augmentation techniques to generate synthetic timeseries data and evaluate their similarity to experimental data. This study will examine the capabilities of different generative models (e.g., Generative Adversarial Networks (GANs)) to create synthetic data that accurately reflect real material and structure behaviours. By assessing the effectiveness of various augmentation techniques, this study will provide a better understanding of how those models perform and can be validated.

The work includes the following tasks:

  • systematic review to identify and analyse state-of-the-art data augmentation techniques in the timeseries domain.
  • explore various generative models, including techniques for adding noise and permutations and generating entirely new synthetic data.
  • assess generated data by comparing the similarity between synthetic and experimental timeseries data using appropriate metrics and performance measures.
  • optional: Validation of the developed methods using experimental tests.
Das erwarten wir von Ihnen:
  • degree in aerospace engineering, mechanical engineering, computer science, mathematics, robotics or comparable degree programmes.
  • very good programming skills in the Python programming language.
  • experience in the field of machine learning and data analysis.
  • personal responsibility, structured way of working and commitment.
  • good command of English (written and oral) desirable.
Unser Angebot:

Das DLR steht für Vielfalt, Wertschätzung und Gleichstellung aller Menschen. Wir fördern eigenverantwortliches Arbeiten und die individuelle Weiterentwicklung unserer Mitarbeitenden im persönlichen und beruflichen Umfeld. Dafür stehen Ihnen unsere zahlreichen Fort- und Weiterbildungsmöglichkeiten zur Verfügung. Chancengerechtigkeit ist uns ein besonderes Anliegen, wir möchten daher insbesondere den Anteil von Frauen in der Wissenschaft und Führung erhöhen. Bewerbungen schwerbehinderter Menschen bevorzugen wir bei fachlicher Eignung.

Weitere Angaben:
  • Eintrittsdatum: 01.08.2024
  • Dauer: zunächst befristet auf 6 Monate
  • Vergütung: je nach Vereinbarung
  • Kennziffer: 93942

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