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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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University of California, San Francisco | San Francisco, California | United States | about 10 hours ago
(e.g., liberal arts, economics, public policy, and/or pre-medical background) and / or equivalent experience / training Skills to learn organization-specific and other computer application programs Basic
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop
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sound understanding of data evaluation Prior experience with single-cell data analysis, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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in neuroimaging, applied data science and/or machine learning are desirable. Funding & how to apply The scholarship will fund course fees up to the value of home fees*, a tax-free stipend in line with