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focused on identifying new therapeutic approaches in patients with ovarian cancer using genomic and functional studies on primary tissues and animal models. We are looking for a Research Assistant to join
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mechanisms underlying vascular endothelial dysfunction in the pathogenesis of atherosclerosis and aortic aneurysm. The lab utilizes advanced redox omics, vascular imaging, and preclinical models to dissect
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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Overview We have an outstanding opportunity to make an impact in modelling of particulate processes for the pharmaceutical industry. We aim to create models for powder-based manufacturing
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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specializing in hydrologic modeling, water quality, carbon dynamics, and CO2 and CH4 emission modeling. The successful candidate will contribute to developing and evaluating aquatic carbon dynamics and CO2 and
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/LSA visitors. This role oversees the daily operations of the Navigation Desk and its student staff, and provides training and support around the Hub's Navigation Services Development Model
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 271598471 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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of Emmanuel Katsanis, MD to pursue studies in transplant and tumor immunology. Research involves murine bone marrow and peripheral blood stem cell transplantation and tumor models. Studies may also include use
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available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace