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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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a very active AI community including industry-led AI research groups (Google, Meta, DeepMind, Microsoft, Samsung, ServiceNow, Borealis) as well as a thriving start-up community. The CERC nominee will
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systems. Montreal is home to a very active AI community including industry-led AI research groups (Google, Meta, DeepMind, Microsoft, Samsung, ServiceNow, Borealis) as well as a thriving start-up community
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, and YouTube. Familiarity with social media management tools (e.g., Sprout Social, Hootsuite, Later) and analytics platforms (e.g., Meta Insights, Google Analytics). Strong skills in short-form video
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Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search
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research environment focusing on integrating multi-source data and developing novel algorithms to address the challenges posed by global environmental change. You will focus on integrating experiments, field
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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer
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hyperparameter optimization, meta-learning, and adversarial training. The general bilevel problem can be written as: min F (x, y∗(x)) where y∗(x) = arg min f (x, y), d