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and develop complex, custom Artificial Intelligence models and applications. This role incorporates Machine Learning, Deep Learning, Computer Vision, Large Language Models, and Agentic AI technologies
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modeling builds on agent-based modeling but focuses on relations, not agents, and emphasizes the role of the researchers and their tools in constituting the model and the generated knowledge (Schlüter et al
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: Immunology, Animal Modeling, and Pathogen Unit. These units directly support resident DHVI/RBL faculty and are also available to support Duke faculty and their collaborators as fee-for-service shared
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+ years of experience with agent-based models or physics-based models 5+ years of experience managing a scientific team of 5 or more people 2+ years of experience working with a product management team to
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, OpenDSS, Machine Learning based emulators, or agent-based modeling. Knowledge of scenario development, resilience frameworks, and socio-environmental-technological systems. Experience with visualization
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Limitation:Temporary (3 years) Contract:TV-L Your Tasks Develop and apply mathematical models (including microsimulation, agent-based, state-transition models) to simulate tools and interventions used before, during and
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Howard Hughes Medical Institute - Janelia Research Campus | Virginia Beach, Virginia | United States | 2 days ago
qualifications and experience as they relate to this position. This should include a description of a deep learning project that you have executed, ideally a creative use of a transformer-based or related
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this we will be creating agentic AI models and in a joint effort between the Research Group for Genomic Epidemiology at DTU National Food Institute and Section of Epidemiology, Department of Health, UCPH we
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decision-support environments). Knowledge of network science, knowledge graphs, temporal networks, graph-based modelling, or complex systems; Experience with simulation (e.g., epidemiological spread, agent
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relying on up-to-date research, the program strives to help growers produce high quality vegetables while minimizing pesticide inputs. The program also develops real-time GIS-based predictive models of pest