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be responsible for applying computational chemistry, chemoinformatics and machine learning approaches to drug discovery, including molecular modelling, virtual screening, molecular dynamics and
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 13 hours ago
or physics-informed machine learning, develop robust surrogate forward models to predict the coupled physical process of GCS, such that we can efficiently forecast the spatial-temporal patterns of the
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candidate with a strong background in robotics, computer vision, or machine learning who is passionate about developing intelligent robotic systems and translating scientific concepts into working
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real-world problems. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or
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-world problems. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading
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interact with the three-dimensional world. We develop methods in computer vision, machine learning, and multimodal representation learning that enable autonomous systems to reason about objects, people
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Title: Modelling dynamics and reactivity with Machine Learning Interatomic Potentials Supervisor: Professor Fernanda Duarte Start date: 1 st October 2027 Applications are invited for a fully funded
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learning” • Participation in ongoing research projects as well as the development of new research initiatives in the fields of mathematics of machine learning, optimal transport, classification algorithms
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quantum attacks Verifiable and privacy-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and