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Field
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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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-quality datasets that are structured and annotated for simulation and machine learning. Develop Digital Twins and simulation pipelines for virtual validation and predictive performance assessment. Implement
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
permeability of fluid flow through hydrate-bearing sediments, • High resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
The ideal candidate would have some, but not necessarily all, knowledge of and skills in: Familiarity with artificial intelligence, machine learning, physics-informed neural networks, or reduced-order
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 3 hours ago
migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas
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collaboration with Prof Yulan He (KCL computer science) and her NLP group. The post is central to the NAVIGATE research programme, which investigates how different modes of AI uncertainty expression affect the
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learning and multivariate decoding of neuroimaging data to predict subjective experiences and individual differences. Successful candidates will be supported in building a research program at the
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different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement learning, as
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application d) Previous experience with computational modelling and machine learning. Additional optional skills and qualifications: Experience with Soft and Living Matter. Contracting requirements
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contribute to project reporting. Work with the interdisciplinary team of the group (theory, computation, machine learning) and support junior researchers on SOT-related topics. Requirements: Education: PhD in