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at the top venues of machine learning research. Responsibilities and qualifications You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in one
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systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine first-principles modeling, machine-learning
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of Engineering Science. The post is funded by EPSRC and is fixed term to the 31st January 2027. A2I explores core challenges in AI and machine learning to enable robots to robustly and effectively operate in complex, real
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WaterWeave project, which focuses on innovative solutions for monitoring and the sustainable management of water resources. The fellow will develop machine learning and cloud computing techniques to estimate
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at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning
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Postdoc (f/m/d) on Generative AI for de Novo Protein Design / Completed university studies (PhD) ...
for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. The Department of Machine Learning for Infection and Disease is looking for a Postdoc (f
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in bioinformatics, machine learning, single-cell omics, statistics, or genomic medicine, and a keen interest in obesity, diabetes, and cardiovascular disease. Background The Novo Nordisk Foundation
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours 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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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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on the true, astrophysical candidates is a computational needle in a haystack. To tackle these “big data” challenges, astronomers have begun to employ machine learning techniques. The application of machine