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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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-omics and Longitudinal Modeling in Alzheimer’s Disease Appointment: Full-time, 1-year term (renewable pending funding and performance) Position Overview We are seeking a highly motivated Postdoctoral
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analysis and Multiphysics Computational modeling of metal alloys using an Integrated Computational Materials Engineering (ICME) approach. Responsibilities: 60% Perform research on the microstructural
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quantification and model calibration for decision-making Familiarity with interdisciplinary work at the interface of climate, environment, and health. Special Instructions Please submit the following materials
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research under PI mentorship. Design, execute, and analyze translational studies focused on hereditary hearing loss and LOXHD1 dysfunction. Develop and characterize preclinical models, including mouse models
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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remote sensing approaches using ocean-color and thermal-infrared satellite observations to study marine organosulfur compounds and ocean–atmosphere interactions. Key research activities include algorithm
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several cancer research groups represented, including joint seminars and other collaborative activities. The group uses various data sources and modern techniques to improve predictive modelling, including
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Functionalization reaction on molecular model substrates Polymer synthesis by chemical post-modification Physico-chemical characterization of polymeric materials at different scales Writing of reports and articles
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 5 hours ago
postdoctoral research associate. The successful candidate will apply a dynamic global vegetation model (DGVM) to the conterminous USA and the globe, to explore future projections of vegetation conditions under