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SHARE@NTU Lab is developing key technologies for humanoid robot research, designed for intelligent service, industrial assistance, and human-centric environments. The key technologies include
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is recruiting a large multi-disciplinary team of postdoctoral researchers for the development of advanced computational imaging techniques and their cutting-edge applications in radio astronomy, space
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, and pharmaceutical discovery. The QUBIC consortium will deliver disruptive innovations across three core quantum domains: * Development of quantum algorithms to solve specific industry
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research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we live in
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, covering the full QT education pipeline. It will also help develop shared standards, qualification frameworks, and incentive structures to drive coherence across a fragmented European landscape. If
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The research activity focuses on the development and experimental validation of advanced Artificial
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collaborative research projects, develop novel computational algorithms, and contribute to high-quality publication and grant-related activities. The position offers opportunities to work in a dynamic research
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours 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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the LLM effectiveness. Key Responsibilities: Research and develop novel ML-based methodologies and algorithms for multilingual and multimodal LLMs. Working closely with other Postdoc/RA/PhD students
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unsupervised techniques, time-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple