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Field
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purpose of this position is to conduct research in the field of model reduction and uncertainty quantification for biological flows. The goal of the project is, in particular, the development of robust
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Applications are invited for a 2-years position as postdoctoral researcher within the research project “Graphs and Ontologies for Literary Evolution Models” (GOLEM), financed by an ERC Starting
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can PI proposals as well as participate in collaborative proposals) who will conduct independent and collaborative research and develop collaborations with faculty, staff, and students. You will have
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broader university. Synergistic professional development opportunities may include co-mentorship of research students in the lab and co-development of grant proposals. Along with mentored research
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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 7 hours ago
. Close interaction with other research groups at the institute, in particular with the nonlinear algebra group of prof. Bernd Sturmfels, will be possible and encouraged. We are currently developing some
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective
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Phenomenology. Members of the group with interest in this area include Professor Ben Allanach and Professor Maria Ubiali. Duties will include developing and conducting individual and collaborative research
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operations, including breeding and genotyping of mice and general maintenance of animal colonies. Develops new ideas for promotion current research for the NJMS –Center for Immunity and Inflammation. Operates
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candidate will work with Professor Guilio Del Zanna on producing advanced atomic models. This post offers an excellent opportunity to contribute to a high-profile project focused on the development
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researcher will work in the research group of Professor Klaus Nordhausen in the project “Signal recovery in noisy spatial data”. The research group develops modern and efficient multivariate statistical