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intelligent transportation systems (ITS). Proficiency in programming, traffic simulation, statistical modeling, and machine learning/deep learning techniques. Excellent communication skills. Ability to work
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deep learning and scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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impact, leveraging one of the highest-quality financial datasets in the industry. What You’ll Do Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and
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electronic health record (EHR) data; apply ML methods (especially deep learning methods) to solve critical medical problems. Implement methods into software that meets research needs, manage and update source
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on the use of new Lyapunov-based deep learning methods. Such development includes: ideation, mathematical development, Lyapunov-based analysis, executing simulations and experiments, and disseminating research
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to learn, thriving in dynamic, fast-moving environment Strong Trading Interest and drive to develop a deep mental model of microstructure and market intuition By applying to this role, you will be
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activities Self-motivated and quick to learn, thriving in dynamic, fast-moving environment Strong Trading Interest and drive to develop a deep mental model of microstructure and market intuition By applying
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strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale