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Field
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
pipeline, building on existing deep learning models in meteorology and climate science Your Profile PhD in computational science, mathematics, physics, or a related subject Experience with machine-learning
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platforms. Experience in development of digital twins or physics-informed machine learning models. Experience in programming (e.g., Python or equivalent) and development of control or data acquisition
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. Preferred Qualifications: Experience in decision analysis and developing computer-based simulations to model either infectious or non-infectious diseases. Evidence of research productivity in mathematical
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and techniques in archaeology. The project has aimed to develop a two-part research programme. The first part focuses on developing and applying methods for 3D digital modelling, ranging from
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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connectivity and graph-theoretic analyses Familiarity with MR sequence programming (Siemens or GE platforms) Machine learning / AI applied to neuroimaging data EEG acquisition and analysis Use of neuroanatomical
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for seasonal prediction using hybrid physics-machine learning models in R&D item Research on Seasonal Meteorological and Oceanographic Forecast Simulator under Development of Integrated Simulation Platform
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual