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Field
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experience contribute to ongoing translational research program related to the application of statistical and machine learning methods in reproductive and perinatal medicine using both clinical quality
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balance of supervised investigation and work experience in a learning environment that will expose the participant to activities across the drug development process. We are seeking scientists from U.S
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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material design process. Some potential key research objectives: AI Model Development: Create machine learning models to predict FGM properties based on compositional gradients and processing conditions
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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record of publications in reputable peer-reviewed journals or conferences in maritime transport, logistics management, machine learning, deep learning, and optimization; Proficient in written and spoken
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
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background/interest in time-series analysis, theoretical machine learning on networks, and high-dimensional statistics. Key Responsibilities: Take the lead in developing sub-projects (problem formulation
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for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena. Experts in