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Field
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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candidates with a proven track record in developing open-source machine learning, deep learning, or cheminformatics tools (Preferred written in Python). Job Duties Plans, directs and conducts research
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particular focus on applications relevant to the Arab world. The successful applicant will join a multidisciplinary research team working at the intersection of machine learning, algorithmic fairness, human
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Computational complexity in adiabatic quantum computation AI to boost quantum technologies (e.g., machine learning for quantum error prevention/mitigation/correction) Quantum machine learning Quantum cloud
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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, Machine Learning, Electrical Engineering, Mathematics, Statistics, Operations Research, and Econometrics. The initial appointment is for one year, but may be extended for an additional year subject to
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(analysis/ geometry), applieddifferential geometry and machine learning;•Solid publication record in image analysis, scientific computing or machine learning.•Knowledge and practical experience in at least 3
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy