96 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Rutgers University
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preclinical models and retrospective human MTLE datasets. Apply complex signal-processing approaches to analyze animal and human intracranial EEG. Develop and implement computational network analyses
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of the Postdoctoral Associate is to conduct research aimed at identifying metabolic vulnerabilities in KRAS-driven lung tumorigenesis, with the goal of informing potential therapeutic strategies for KRAS-mutant lung
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include but is not limited to 1) developing, validating, improving and applying cell-based and other preclinical models to understand the functions of lipids, lipoproteins and related factors in influencing
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group led by Dr. David Klinges in the Department of Ecology, Evolution & Natural Resources. Our work combines computational modeling, field studies, and laboratory experiments to understand how
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modelers, epidemiologists, and economists. Development, calibrate, and extend of microsimulation and compartmental models of tobacco use and lung cancer in R. Receive training in modeling methods
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Sahoo and perform molecular/cellular studies of neuronal development using in vivo (mouse models) and in vitro (cultured primary neurons) models, involving skilled surgical procedures on mice and rats
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include (but are not limited to) developing/applying new approaches to improve range shift projections under climate change, regionally modelling organism-environment interactions across species ranges
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modelers, epidemiologists, and economists. Development, calibrate, and extend of microsimulation and compartmental models of tobacco use and lung cancer in R. Receive training in modeling methods
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hematological neoplasms and aberrant cell signaling. Utilizing multi-omics, mouse models (GEMMs/PDXs), and human biospecimens, the incumbent will conduct independent research to uncover novel therapeutic targets
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sequence and to model the evolution of body size using bounded evolutionary models. 3) Biomechanical and Hydrodynamic Modeling: Apply computational fluid dynamics to test hydrodynamic hypotheses related