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Field
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Architecture: Architect, build, and evaluate multi-agent systems powered by LLMs and machine learning, incorporating Orchestrator and Memory agents, vector databases, and knowledge graphs to enable complex goal
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adaptive cybersecurity solutions for shared social and economic systems. The successful researcher will apply expertise in programmable networking, network traffic analysis, and machine-learning
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will contribute to all stages of the research process, including literature review, conceptual framework development, data collection and preparation, AI and machine learning model development
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statistical methods, computational models and/ or machine learning algorithms to analyse environmental and infectious disease data; - Collaborate with multidisciplinary and international teams of researchers
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research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to any
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skills, including statistical analysis using R, Python, SPSS, Stata, or equivalent software. • Experience with computational social science methods, including NLP, machine learning, LLMs, social media
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develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining
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: multimodal AI, machine-learning, human annotation, multimodal embedding generation, model training and refinement, large-scale inference pipelines, or reproducible computational workflows. Strong programming
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within the Division of Artificial Medical Intelligence of Department of Ophthalmology in the University of Colorado School of Medicine. We focus broadly on quantitative and machine learning techniques in
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artificial intelligence and machine learning (AI/ML) models that predict therapeutic response, identify clinically actionable patient subgroups, and support personalized treatment strategies. Through