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. Experience of current Artificial Intelligence and Machine Learning methodologies/tools e.g. agentic patterns, LLM orchestration, vision models etc. Desirable Criteria: MEng, MSc or Ph.D. in a relevant area
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others. Excellent and ambitious JD, LLM, and SJD students with strong entering credentials. A three-course teaching load, small average class sizes, generous support for research conference travel, and an
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captured in a structured, machine readable dataset to support AI model development and give feedback on material realizability. Where to apply Website https://www.academictransfer.com/en/jobs/364461/phd
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or sponsors. U.S. Citizenship with the ability to obtain and maintain a security clearance Desired: Exposure to Retrieval‑Augmented Generation (RAG), vector databases, embedding pipelines, or LLM‑enabled
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Generation (RAG), vector databases, embedding pipelines, or LLM‑enabled systems. Familiarity with network science or graph analytics concepts, including: Graph modeling and analysis using tools such as
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degradation, which require collaborative cross-border approaches. This Post-doctoral Research Fellow Level 1 post is funded by the Climate+ Co-Centre (https://www.climatecocentre.org/ ). The successful
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field evaluation connect; a minimum offline/shared-device architecture and data flow; the role and limits of AI, including one capability that should not use an LLM; safeguarding, consent/assent, referral
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accelerators. The project will address the following key areas: Transformer-based models, including Large Language Models (LLMs), Vision Transformers (ViTs), and Vision-Language Models (VLMs), are now central to
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analysis, or assistive technology; Experience in applying AI methods such as machine learning, deep learning, computer vision, multimodal data analysis and large language models (LLM) in health-related
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. Design methods for security analysis and verification of shared data and models. Develop tools for data processing, LLM training and security evaluation. Write research papers. Other duties as assigned