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based on an orchestrator and a set of intelligent agents for information retrieval and processing; - Validation of proposed solutions through empirical studies in representative decision support and
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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under the Principal Investigator Assistant Professor Borame Dickens alongside multiple collaborators and experts. Methods include agent based/individual based modelling, SEIR modelling, geospatial
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conditions and policy outcomes. Desired areas of expertise include: dynamic and complex systems, agent-based modeling, computer programming (familiarity with R, Python, Netlogo), statistical analysis
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aging shape communicative breakdowns and adaptations in interactions involving large language model (LLM)-based conversational agents. The candidate will conduct mixed-methods research, including
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proficiency, particularly in Python Experience with or strong interest in, Language models (SLMs/LLMs), Agentic AI systems, Deep learning architectures, Multimodal AI (e.g., text, time-series, sensor data
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Japan and North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia
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programming will be advantageous. Knowledge of intelligent decision agents based on graph neural network or similar will an advantage. Key Competencies Good knowledge in reliability analysis. Experience in
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analysis, activity-based modelling, and integrated transportation and land use modelling. Demonstrated experience in econometrics and agent-based microsimulation for large-scale regional travel demand
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | 21 days ago
high-volume data to prepare for modeling; Learn how to adjust model structures/parameters based on calibration data, and evaluate model credibility using validation data; Learn how to execute modeling