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welcomes applications from individuals with a master’s degree in the following fields: Computer Science Mathematics Natural or engineering sciences or an equivalent degree, interested in High-Performance
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the International Continental Scientific Drilling Program (ICDP) and aiming to study the icehouse–hothouse transition during the Permian (299–252 million years ago) and extreme continental climate states. Key
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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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, e.g. through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center
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disciplines. For details please see the programme information for the Philipp Franz von Siebold Award. Programme information (PDF, 119 KB) List of award winners The sponsorship The award amount is €80,000
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environment. The candidate will be given the opportunity to develop a doctoral thesis with extensive support through the RTG2413 (www.synage.de ) and the CBBS graduate program (gp.cbbs.eu ). Your profile
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Friedrich Wilhelm Bessel (1784–1846) and funded by the Federal Ministry of Education and Research. For more information please see the Friedrich Wilhelm Bessel Research Award programme information. Links
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Max Planck Institute for Medical Research, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 5 days ago
Job Code: 15/2025 Job Offer from September 09, 2025 We, the Max Planck Institute for Medical Research, are one of 84 institutes and research centers of the Max Planck Society (MPG) for the advancement of science. Our focus is on basic medical research. We currently employ around 270 people in...
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reactive agents and biofilms, which are anticipated to play a key role in climate preparedness in water resource management. If you want to advance computational hydraulics while tackling an urgent
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in