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holistic views of complex chemical and biological systems. However, discrepancies in data structure, measurement scales, and resolution across platforms make this integration challenging. Existing approaches
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life, gradual performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis
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applications Multimodal data integration and prediction, including clinical, medical imaging, and sensor data (e.g. ECGs) The PhD student will have the opportunity to conduct both methodological and applied
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circuitry during behavior, to the quantification of natural animal behaviors. We are looking for a PhD student to work on a joint project between the Neural Information Flow (Scholz) and Genetics of Behavior
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Are you passionate about applying computational approaches to solve problems within chemistry and biology? We are now looking for an Industrial PhD student in Data-Driven Life Sciences to work on a
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Max Planck Institute for Neurobiology of Behavior - caesar, Bonn | Bonn, Nordrhein Westfalen | Germany | 19 days ago
Information Flow (Scholz) and Genetics of Behavior (Lightfoot) groups. Using a comparative approach, we aim to understand how evolution has adapted existing behaviors and invented novel ones through alterations
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-mounted ground-penetrating radar (GPR) with smart meter data to transform leak detection. Drones equipped with GPR will identify underground anomalies, while smart meter data will refine localisation
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inclusive research groups. Your role The UBIX Research Group is searching a PhD candidate to work on data-driven sustainable energy systems. The successful candidate will join the UBIX Research Group, led by
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of shared local, state, national, and global communities. More information about ASU’s Charter and Design Aspirations is available at the following link: https://newamericanuniversity.asu.edu/about/asu
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results, further research is guided by trial and error with the goal of deriving intuitive trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular