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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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and Correction of Industrial Design Flaws – Towards Fail-Safe Industries) is a Business Finland funded research project with the objective to develop a new type of computer-aided design platform
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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. This field encompasses Computer Science, Data Science, Artificial Intelligence, and related interdisciplinary areas, with a focus on computing technologies, software development, algorithm design, and
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under
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communications signals. The objective of QUESTING is to develop new methods for quantum networking, fault-tolerant design and resource-efficient hybrid systems by training new generations of Q-System Innovators
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, funded by FAR 2024 UNIMORE linea FOMO, and aims to develop authenticity models through environmentally friendly analytical techniques combined with data processing and machine learning algorithms
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We are seeking an ambitious candidate to develop Machine Learning models and frameworks for time series