Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 11,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!For our
Institute of Atmospheric Physics in
Oberpfaffenhofen near Munich we are looking for a
Meteorologist, Physicist or similar (f/m/x)
Lifecycle forecasting of deep moist convection using physics-based machine learningWhat to expect: Deep moist convection constitutes one of the most important atmospheric transport mechanisms and is associated with several extreme-weather events (hail, heavy precipitation, wind gusts, …). As a consequence of climate change, the severity of these events is expected to increase in the future. Spatiotemporally highly resolved short-term predictions of deep moist convection are an essential tool to limit significant social and economic damage. Applications range from automated transportation to optimal routing of air traffic. The objective of this PhD thesis is the development of a more accurate, robust and physically consistent short-term forecasting method for deep moist convection. The starting point will be a prototypical prediction method currently under development, in which we combine physical model building with deep learning. In a first step, the existing model shall be extended to the use of data from the new Meteosat Third Generation (MTG) satellite mission, of which we are among the first users. In a second step and in close collaboration with the DLR Institute of Data Science in Jena, uncertainties and explainability of the model will be established. On the basis of these analyses, potentials for improvement shall be identified and implemented. This work may benefit from concepts investigated in an ongoing PhD thesis and shall explicitly exploit synergies. In addition to thorough validation of the model, case studies in aviation demonstrators are integral part of the project. What we expect from you:
- completed scientific university degree in physics or meteorology (diploma/Master) or a comparable subject
- good knowledge in atmospheric physics and/or statistical physics
- good knowledge in programming, ideally in Python
- ideally, initial experience with machine learning
- knowledge in (passive) remote sensing and statistical data analyses are a plus
- very good knowledge in English
- high level of independence and team spirit
DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified. Further information: Starting date: immediately
Duration of contract: 3 years
Type of employment: Part-time Remuneration: up to German TVöD 13 Vacancy-ID: 98452
Contact:
Tobias Bölle
Institut für Physik der Atmosphäre
Tel.: 08153 28 1815
