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architectures (e.g., convolutional neural networks or transformers); deep specialization is not required. Exposure to Generative AI concepts and large language models (LLMs) is a plus. Proficiency in Python
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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seeking a Research Assistant to support applied research at the intersection of healthcare, artificial intelligence, and data analysis The primary focus of this role will be large language model (LLMs) and
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capacity. Requirements and Qualifications Bachelor’s degree required; JD, LLM and/or other graduate training, law practice and/or management experience strongly preferred. Requires analytical, written and
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include now commercial large language models (LLMs), but their applications extend to time-series data, audio, and video. The potential of foundation AI models for power systems has already been recognized
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such artificial empathy, and how this relates to AI sycophancy and mental health. For more information about the research conducted in the lab, please visit the lab website at https://cascoglab.psy.utexas.edu
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AI workloads: embeddings, vector search, RAG, LLM orchestration pipelines. Experience with ETL/ELT frameworks, API integrations, and event streaming (Kafka/EventHub). Understanding of data modeling
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, generative AI, large language models (LLMs), natural language processing, automation, and AI-enabled platforms. Demonstrated experience in governance, ethics, fairness, transparency, and explainability
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, FlexJD, LLM, Online LLM and MLS programs. The Associate Dean is responsible for the development and implementation of a sophisticated strategic enrollment management plan for the School of Law. The
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relevant social science discipline and research experience. A JD or LLM in addition to a Ph.D. is preferred. A history of successful teaching at the graduate level and experience teaching online is strongly