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, Modeling, & Method Development – 40% - Select features, build, and optimize classifiers using machine learning techniques. - Perform statistical analyses with both traditional methods (e.g., survival
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Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent academic record, including
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. Must be detail oriented and possess basic mathematical skills. **Position is On-site - Possible remote - Following on site training, minimum on-campus schedule of one day each week, subject to change
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, classification, and report generation. Participation in sample preparation and image acquisition in order to understand the data generation process and optimally integrate it into model development. Close
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We are seeking a highly motivated doctoral student to develop ship physics-integrated machine learning models for real-time prediction and optimization of wind-assisted ship propulsion systems
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aspects of modern portfolio theory, asset selection, allocation, and rebalancing. Both the mathematical and behavioral aspects of portfolio construction are studied. Risk assessment and optimal combination
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is inherently multi-disciplinary, Business Analytics lies at the intersection of Computer Science, Statistics, Machine Learning, Big Data, Applied Mathematics (particularly Optimization), Operations
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(perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity methods to discover a wide range of high
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different professional disciplines to develop a broader understanding of how to improve the performance of the entire supply chain. Specific issues to be discussed include: supply chain design, optimization
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the charging schedule (V1G) to consume energy at optimal times. Another form incorporates equipment that allows the vehicle to discharge energy back into the household or the grid (V2X). This project aims