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collaboration with the team in Tübingen and with the parallel team based in Leuven. The candidate will collaborate with research carried out within the same period at KU Leuven by T. Nelis (FitMA) on the earlier
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Apply now Job no:535376 Work type:Faculty - Pro Tempore Location:Eugene, OR Categories:Instruction, Computer and Information Science, Data Science Department: CAS CIS Computer & Information
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 21 days ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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. Major responsibilites: The candidate will work independently to purify recombinant IDR constructs and label them for F19-NMR analysis. In parallel, the candidate will use Python for bioinformatics
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of Psychology at the University of Liverpool. This position focuses on the computational modelling side of the project. You will work on extending an existing model of noun and verb acquisition (Brusini et al
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empirical evidence. The project runs for 33 months and comprises three research components operating in parallel, each led by a postdoctoral researcher and integrated through monthly team meetings and
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empirical evidence. The project runs for 33 months and comprises three research components operating in parallel, each led by a postdoctoral researcher and integrated through monthly team meetings and
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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disciplines; the prominence of chaos theory in the 1970s and 1980s; and its longer-term methodological and conceptual impact. The historical research is intended not simply as a parallel study, but as a source
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exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and