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Deadline 15 Apr 2026 - 00:00 (UTC) Country United Arab Emirates Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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University of California Los Angeles | Lake Arrowhead, California | United States | about 1 month ago
This Employer https://main.hercjobs.org/jobs/22071468/guest-services-agent Return to Search Results
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position involves assisting in the development, deployment, and evaluation of AI-based models and tools for engineering applications. At the end of the project, whole work repository needs to be uploaded in
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develop a framework based on collaborative AI concept, e.g., Agentic AI or other collaborative Large Language Models (LLMs), that synthesizes actionable intelligence from distributed, heterogeneous data
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of performance improvement and evidence-based practice. Basic computer skills. Ability to assess, plan, implement and evaluate patient care, taking into consideration protective interventions, development
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, age or social background. Please read our application guidance to see our programme specific requirements, including proforma Research and Personal Statements: https://biodtp.norwichresearchpark.ac.uk
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retirement contributions that add to your bottom line. Total Compensation Calculator: http://www.cu.edu/node/153125 Equal Employment Opportunity Statement: The University of Colorado (CU) is an Equal
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. Experience developing and fine-tuning Large Language Models (LLMs) and transformer-based architectures. Practicalexperience building AI agents, including agentic workflows, autonomous decision-making pipelines
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engineering projects is increasingly complex, particularly when balancing human expertise and Large Language Models as collaborative agents. This project aims to propose a reputation-based agentic Artificial
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integration across identity management, research computing platforms, AI/ML workloads, and a growing portfolio of cloud-native and hybrid applications. This hands-on technical leadership role requires deep