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Field
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mission standards. Core challenges: translating advanced algorithms into field deployable and operational systems managing complex technical integration across multiple consortium partners and international
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 4 hours ago
in artificial intelligence (AI) for settings involving multiple interacting decision-makers---whether autonomous AI agents, humans, or a combination of both. Applications include mixed-autonomy
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for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making
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Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
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of approximately 1.7 million square feet and high-performance computing facilities at the DOD Supercomputing Research Center. This opportunity has multiple projects based out of the ERDC Field Research Facility in
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy