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Field
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adversarial learning. Working on an exciting research project focused on developing continual and robust VLMs, you will investigate novel approaches to continual learning, model pre-training and post-training
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structural design. (3). Realize hierarchical structural decoupling via dual-network engineering to break the trade-off between mechanical robustness, wave absorption, and thermal conductivity. (4). Investigate
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. The Risk and Infrastructure Systems Lab, led by Asst. Prof. Alex Sixie Cao, develops quantitative methods for understanding and managing the reliability, robustness, and safety of structures and
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to publications in top-tier international conferences and journals, as well as real-world implementations. The role includes designing novel algorithms, building robust software systems, and collaborating with
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evaluations to explore how interventions operate in practice and how they are experienced by those involved. • Supporting organisations to develop robust internal evaluation capacity, including
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The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE
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-separation methodology for efficient plasma delivery to electrochemical sensors. • Characterize plasma separation performance including separation time, yield, cell carryover, and robustness across
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support researchers in constructing robust behavioural models without replacing scientific judgement. The emphasis is on interpretable AI processes that complements econometric theory and domain expertise
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project – “Recommender systems in the era of Large Language Models (LLMs): Method, vulnerability, and robustness”. Qualifications Applicants for the Senior Research Fellow / Senior Project Fellow post
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Conduct research in adversarial machine learning, AI security and the robustness of deep