Aerospace Engineering Student Position

Vor 2 Tagen

Stuttgart, Baden-Württemberg, Deutschland Deutsches Zentrum für Luft- und Raumfahrt (DLR) Vollzeit
About the Position

We are seeking a highly motivated student of aerospace engineering, chemistry, or a related field to join our team at the Deutsches Zentrum für Luft- und Raumfahrt (DLR). As a research assistant, you will be working on the development of IR-based machine learning models and training databases for property estimation of sustainable aviation fuel (SAF).

Sustainable aviation fuel (SAF) usage enables new opportunities for reducing aviation's climate impact. The required fuel properties, such as aromatic content and net heat of combustion, vary between SAF types, and costly laboratory analysis is typically required to determine these properties. To address this challenge, we are developing an IR-based estimation method that captures the variance of current and novel aviation fuels. This method has shown high potential for filling information gaps in real-time using only a few drops of the fuel sample.

Your Tasks
  • Literature review of conventional and sustainable aviation fuels and current IR-based models
  • Conceptualization of the data set strategy for different use cases, such as property estimation and fuel classification
  • Iterative performance of IR measurements to evaluate IR-sensor configurations and ML models
About the Team

As a member of our team, you will have the opportunity to work in a dynamic and inspiring environment with a diverse group of researchers from over 100 nations. Our institute offers a wide range of training and development opportunities to support your personal and professional growth. We value diversity, respect, and equality for all individuals and strive to create an inclusive work environment.

Optional: Different measurement automation methods may be evaluated during the study.

Requirements
  • Student of aerospace engineering, chemistry, or a related field
  • Basic programming experience in Python and machine learning models
  • Basic theoretical or applied knowledge in organic chemistry and IR spectroscopy

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