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to the best research institutions around the world. Continuous scientific mentoring by your scientific advisor as well as feedback and wide-ranging expertise from the whole group in multiple facets of quantum
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, photosynthesis, metabolic engineering, breeding, and whole plant phenotyping of dedicated energy crops such as giant miscanthus, switchgrass, hybrid poplar, willows, or bioenergy oil seed crops. Research
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, bioengineering, chemical engineering, microbiology, or other relevant backgrounds are preferred. You will have the opportunity to collaborate with multiple academics and early career researchers in biofilm
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Science or German Diplom, preferably in biology, chemistry/biochemistry, psychology, medicine, physics, engineering, or informatics neuroscientific knowledge and interest in an interdisciplinary research
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of research include integrating AI-enhanced weather forecasts with crop models, exploring tools to guide land allocation and nitrogen management, or developing dashboards that fuse multiple data streams
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group has a strong international reputation in academia and industry for pioneering column store technology, fast compression methods, vectorized query execution, indexes for interactive data analysis
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the broad area of analytical database management systems. Our research group has a strong international reputation in academia and industry for pioneering column store technology, fast compression methods
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Degree PhD Course location Potsdam In cooperation with This programme is offered by the Faculty of Digital Engineering, a faculty jointly founded by the Hasso Plattner Institute (HPI) and the
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The Max Planck Institute for Neurobiology of Behavior – caesar • | Bonn, Nordrhein Westfalen | Germany | about 8 hours ago
(or equivalent) from any relevant field (life sciences, mathematics, physics, computer science, engineering, etc.) to be immersed in a stimulating environment that provides novel technologies to elucidate
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy