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researcher for a two-year position within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). By leveraging agentic Large Language Models (LLMs), the project will develop
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems for viral agents. Emphasis is placed on determining
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, Uncertainty, and Dynamics.” About the Project Market interaction is increasingly automated by artificial learning agents. Examples include pricing agents in electronic retail or bidding agents in display
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parameter space, and using and/or developing agent-based models for the movement and behavior of fish in rivers. Presenting material at conferences, writing research papers for publication, and/or assisting
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outcomes under different market design scenarios. The research will combine machine learning, stochastic optimization, and agent-based modelling with behavioural experiments. Case studies from emerging
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, cytocompatibility testing and evaluation of cellular response to therapeutic agents, as well as assisting with technical documentation and preparation of scientific publications, in the scope of the Élpis project
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spatio-temporal generative models, and multimodal foundation models—including vision-language MLLMs and agentic AI frameworks—for longitudinal MRI and clinical data. Fellows will help build next-generation
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controllability • Learning and calibration strategies for uncertainty-aware language model prediction • Knowledge-augmented and neuro-symbolic approaches for language-based reasoning • Evaluation and design of LLM
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
distance of their assigned duty station. The Data-Driven EnviroLab (DDL) is an interdisciplinary and international research initiative based at UNC’s IE that is redefining how data is used to tackle