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on developing robotics and cyber physical systems solutions using machine learning and artificial intelligence to support different aspects of marine science, with opportunities to expand to other areas
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, headed by Laura Cantini, works at
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Science in Earth Observation develops innovative signal processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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validate data-driven methods that combine production data, predictive and state-estimation models, uncertainty-aware learning and constrained optimization to improve process consistency under process drift
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NTU’s global reputation for excellence in robotics and AI. Key Responsibilities Design and implement VLA models and world models. Develop and optimize deep learning algorithms to enable robotic arms
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for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems
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learning and experimental validation. Key Responsibilities: Develop AI-guided experimentation methods integrating data, predictive modelling, uncertainty and validation. Build structured materials/process
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Job Requirements REQUIRED: PhD in a field of quantitative science; strong background in machine learning and signal processing; experience with and commitment to rigorous and reproducible