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University of Massachusetts Dartmouth | Dartmouth, Massachusetts | United States | about 20 hours ago
settings. We are seeking a dynamic and interdisciplinary scholar with expertise in science education and/or the learning sciences, and demonstrated experience with NetLogo or similar agent-based modeling
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/01 11:59PM (posted 2025/08/21, listed until 2025/12/01) Position Description: Apply Position Description MIT Multi-agent AI Postdoctoral Fellowship Program Recent years have seen a surge of interest
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be developing and applying advanced AI models and sophisticated multi-agent systems to address pressing challenges in regulatory genomics. Key research areas include: Mechanisms of Common and Rare
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to facilitate the management and editing of the different specialist agents that make up the system. - Implementing mechanisms to generate artificial conversations, based on historical case studies, for training
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experience in software development. Experience applying large language models (LLMs) or autonomous agents to scientific tasks such as code generation, protocol reasoning, or automated experimental planning
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software development. Experience applying large language models (LLMs) or autonomous agents to scientific tasks such as code generation, protocol reasoning, or automated experimental planning. Proven ability
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. This will include: Developing spatial community detection tools to identify functional hubs and cellular ecosystems in tissue. Implementing dynamic modelling frameworks, combining agent-based models and graph
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with NERSC staff, domain scientists, and partners at NVIDIA and Dell to prepare high-impact workflows for 12,000+ NERSC users. What You Will Do: Contribute to one or more NESAP AI-based scientific
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templates for distributed AI training, agentic AI with modeling and simulation, and end-to-end workflow monitoring, profiling, and optimization. Working with quantum simulation tools, including NVIDIA CUDA-Q
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the economic benefits for individuals and communities when interacting with the energy markets. AUTONOMY will pioneer a novel transfer learning approach to exchange expert knowledge amongst energy manager agents