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cues play a role analogous to conditioned stimuli: they are signals that, once learned, allow the agent to anticipate the consequences of its actions. Scientific Motivation: Learning-based navigation
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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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. The modelling will
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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. The modelling will
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verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based automated reasoning tools such as ESBMC (https://github.com/esbmc/esbmc ) to address safety and reliability
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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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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in
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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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: 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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, OpenDSS, Machine Learning based emulators, or agent-based modeling. Knowledge of scenario development, resilience frameworks, and socio-environmental-technological systems. Experience with visualization