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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 2 days ago
Job Code: 396 Job Offer from September 08, 2025 We invite applications for multiple postdoctoral fellowships at the Max Planck Institute for Astronomy in Heidelberg, Germany including independent
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Research FieldChemistry » Organic chemistry Additional Information Benefits Net salary ~ 2200 €/month Contract period: 18 months Selection process Application - Detailed CV - Short research summary - Cover
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technologies for AC/DC hybrid power systems—alternating current networks that integrate DC networks—and the definition of functional requirements to ensure their performance and reliability. As part of
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leader Pedro G. Ferreira and other members of the Beecroft Institute of Particle Astrophysics and Cosmology. The post holder will be a member of a disparate research network working independently to carry
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investigator and other team members, if applicable, with the proposed instruments. - Analyse data quantitatively (statistics) and qualitatively (coding for interviews). - Co-organize research meetings within
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fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results
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postdoctoral researcher to join our team for a 4-year fully funded fellowship. Our lab is part of the prestigious EMBO Young Investigator Labs network, giving you access to exclusive international collaborations
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dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In
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independently as well as together with the principal investigator and other team members, if applicable, with the proposed instruments. - Analyse data quantitatively (statistics) and qualitatively (coding
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planets, and trans-Neptunian objects in retrograde motion. Traditional orbital dynamics methods will be combined with machine learning techniques, such as convolutional neural networks and Vision