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this centre, we are opening a PhD position on remote sensing of mountain forest dynamics. The Technical University of Munich (TUM) is one of the leading universities globally. At the TUM School of Life Sciences
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, Geoscience, Remote Sensing, Hydrology, Data Science, Physics, or related fields • Experience in machine learning (ML), artificial intelligence (AI) or related fields • Software skills in ML languages such as
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quantum computing as well as experience with cryogenics, signal delivery, microfabrication, materials optimization, and microwave control are highly preferred qualifications Please feel free to apply
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, microfabrication, materials optimization, and microwave control are highly preferred qualifications Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be
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English Strong independence, commitment, and sense of responsibility Reliable and conscientious working style Excellent teamwork and collaboration skills Our Offer: We work on the very latest issues
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synthesis Fluent command of written and spoken English Strong independence, commitment, and sense of responsibility Reliable and conscientious working style Excellent teamwork and collaboration skills Our
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inorganic synthesis Fluent command of written and spoken English Strong independence, commitment, and sense of responsibility Reliable and conscientious working style Excellent teamwork and collaboration
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impact and adaptation analyses, and for spatio-temporal modelling as well as upscaling of ecosystem properties via remote sensing Interactive collaboration and exchange within the TUM Center for Forest
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to remote sensing and simu-lation modeling. A particular focus of our work is on mountain forest ecosystems. A quantitative understanding of ecosystem dynamics provides the foundation for the development
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 22 days ago
) approaches, off-the-shelf solutions cannot be employed given the vast diversity of microbial communities and the limitations in the number of observations from remote deep-sea sites. In this project, you will