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and efficiency of AI-augmented research Operationalize analytic quality as measurable dimensions across multiple contexts Conduct mixed-methods validation including controlled experiments, observational
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experiments to test synthetic data quality, reliability, and resilience under corrupted or adversarial conditions. Participate in compiling and curating large-scale datasets for model training and benchmarking
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or desire to design and implement experiments that probe model vulnerabilities. Experience with NLP/LLMs or generative AI, adversarial ML, software engineering practices (Git, reproducibility, experiment
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systems (symbolic AI + neural networks) is a plus. Experience with spatial sensing methods and technologies and/or some aspect of machine spatial awareness (for example, robotic path planning) is a plus
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systems (symbolic AI + neural networks) is a plus. Experience with spatial sensing methods and technologies and/or some aspect of machine spatial awareness (for example, robotic path planning) is a plus
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, computer science, or a closely related field. Coding experience for the computational modeling of physical and/or engineered systems, preferably with finite-element methods, is a must. Strong programming