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Field
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machine learning with the logical reasoning and semantic understanding of symbolic AI (often referred to as material and design informatics) is being developed for the accelerated discovery and development
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Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and spatial patterns of synergy and
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authorities. École des Ponts ParisTech, in accordance with its strategic plan, develops a long-term research activity in the field of Machine Learning and Computer Vision. The IMAGINE team is a renowned
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will
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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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for this position focused on machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in collaboration with Brookhaven Lab staff, the Department of Energy (DOE), and
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GNSS, wireless navigation and machine learning is desirable Additional skills that are highly appreciated in this position are the willingness to work hard and to learn more about the fascinating
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large