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- Model-based assessment of climate impacts on conflicting land use systems Reference code: 50139755_2 Commencement date: August 1st, 2025 Work location: Hamburg Application deadline: 04.06.2025 The Climate
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: Approximately 2,000 EUR/month for three years Website: IMPRS-ESM Application Contact: office.imprs at mpimet.mpg.de The International Max Planck Research School on Earth System Modelling (IMPRS-ESM) invites
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to the success of the whole institution. At the Faculty of Environmental Sciences, Department of Forest Sciences, Institute of Soil Science and Site Ecology, the Chair of Soil Resources and Land Use offers a
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, your work will contribute to establishing a fundamental understanding of the mechanical properties and microstructure of newly developed advanced ceramic materials for solid oxide electrolyzer cells
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of the research group on “Aggressive Lymphomas” led by Prof. Dr. Georg Lenz in the Department of Medicine A. We offer an excellent research infrastructure with access to state-of-the-art equipment, a
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employees to advance science for society. Join us in breaking new ground and writing success stories –your own and those of our university. Support us as of now in part-time position (65 %) as a Doctoral
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 4 days ago
Job Code: 16/2025 Job Offer from July 24, 2025 The Max Planck Institute for Extraterrestrial Physics (MPE) in Garching is a world leader in the field of ground-based and space-based experimental
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climate states. Key responsibilities To carry out and complete your own research towards a doctoral degree. Design, run, and analyse equilibrium simulations with a coupled Earth-system model of intermediate
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 6 days ago
Job Code: 16/2025 Job Offer from July 24, 2025 The Max Planck Institute for Extraterrestrial Physics (MPE) in Garching is a world leader in the field of ground-based and space-based experimental
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and