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the scope of the project “COIMBRA Sustainable Tourism LLM Large Language Models applied to create better experiences for tourists and local communities, whilst supporting the development of a
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, Teams). Familiarity with design tools like Illustrator, PowerPoint, and Canva is a plus. Creation of Geophysics Database for training Large Language Models (LLMs): Collecting geophysics- and geochemistry
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
and AI-driven Integrity in Mitigation. CLAIM advances the credible use of artificial intelligence, especially generative AI and large language models (LLMs), to accelerate climate mitigation while
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
. CLAIM advances the credible use of artificial intelligence, especially generative AI and large language models (LLMs), to accelerate climate mitigation while safeguarding integrity in climate commitments
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account may cause delays in a hire process. Department: Washington College of Law Time Type: Part time FLSA Status: Non-Exempt Job Description: Summary: The Graduate Admissions Assistant supports the LLM
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. Knowledge of visual generation and multimodal LLM will be advantageous. Key Competencies Proficiency in programming languages (e.g., Python and C++), and familiarity with AI frameworks (e.g., PyTorch). Self
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certification of AI Explainability, interpretability, and transparency of AI models Design and engineering of scalable AI/ML and generative AI systems (e.g. LLMs, multimodal models), including lifecycle aspects
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: “My Vision for AI Driven Procurement Processes” For further information and to make an online application for the above post please visit website visit http://www.tudublin.ie/vacancies Technological
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notice. Applications in paper format will not be accepted. Application deadline: The deadline for applications is from 24 February to 9 March 2026 (10 working days). Where to apply Website https
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: Project A – Synthetic Data for Theory-Driven Behavioral Research This project investigates how large language models (LLMs) produce synthetic responses to psychological experiments and how these compare