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a postdoctoral associate to develop methods and metrics for evaluating how consultation with large language models affects analytical work products and team interaction patterns in complex information
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foundation models for synthetic data generation: Time Series Foundation Models, Variational Autoencoders, Generative Adversarial Networks, and Large Language Models (LLMs, TSFMs, VAEs, GANs). Design
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and their analytical frameworks. 2. Development of mechanisms for tuning Large Language Models (LLMs) and multi-modal foundation models to function effectively in resource-constrained environments. 3
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the spatial reasoning capabilities of multi-mode large language models and hybrid AI systems combining artificial neural networks with symbolic AI. In-house research and development of one of these systems
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the spatial reasoning capabilities of multi-mode large language models and hybrid AI systems combining artificial neural networks with symbolic AI. In-house research and development of one of these systems
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) are looking to hire a post-doctoral associate to carry out joint research in resilience of neural networks, including large language models (LLMs) and other generative AI systems and foundation models
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structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
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Sensing or Networks. The successful candidate will have expertise in the development and application of quantum technologies for sensing and networking, including but not limited to quantum communication
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external research funding and who can interface their research with the department's existing strengths in communications/signal processing, wireless networks, RF/microwave/THz engineering, IoT, AI
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demonstrate potential for securing external research funding and who can interface their research with the department's existing strengths in communications/signal processing, wireless networks, RF/microwave