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, drug screening, mechanism-of-action studies, and in vivo models of fungal infection. Applicants with experience in one or more of the following areas are encouraged to apply: • Fungal biology and medical
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characterisation, and computational modelling to enable structure-driven materials discovery, targeting systems with unconventional atomic arrangements and emergent properties, and is inspired by the recent
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characterisation, and computational modelling to enable structure-driven materials discovery, targeting systems with unconventional atomic arrangements and emergent properties, and is inspired by the recent
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differences and infection on the initation and resolution of inflammation in both in vivo murine models and clinical samples. The full-time post-doctoral position will support NIH-funded research projects
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investigate systemic immunity, the tumor microenvironment (TME), and the mechanistic drivers of resistance to radiation and immunotherapy utilizing advanced pre-clinical models of head/neck and pancreatic
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 22 hours ago
and contract staff. You will learn how to do the following: Design and validate numerical reservoir models for unconventional enhanced oil recovery processes in shale and tight formations. Calibrate and
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diversity of platforms and building blocks. The foundry model is now becoming established where Process Design Kits (PDKs) allow fabless designers to create their own chips with custom functionality. While
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | 2 months ago
-depth experience with modelling approaches, taking into account small sample sizes and repeated measures designs, as well as unbalanced data sets with missing values and categorical variables. Furthermore
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different areas of mathematics. One position will focus primarily on geometric and structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical
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pathogens. This work utilizes advanced epidemiological modelling combined with AI methods for adaptive rather than static surveillance design. This project is particularly exciting because it combines new