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Field
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· Lead the design and implementation of mixture-of-experts neural architectures and reinforcement learning pipelines for counterfactual disease trajectory simulation for EMED, an NIH-funded multi-modal AI
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in close partnership with experimental colleagues across knitted sensor arrays and piezoelectric/triboelectric energy harvesting, developing deep technical understanding of both systems and building
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leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a truly inclusive workplace in our Diversity, Inclusion and Belonging
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling
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field with more than 3 years (principal research engineer), 5 years (research fellow) of post-doctoral or industrial experience. Deep understanding of data sharing fundamentals, including availability
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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networks. • Apply AI/ML and deep learning techniques for analyzing dense multimodal data in real time. • Integrate system-level solutions for interfacing with computers, smartphones, or AR/VR
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record of delivering high-quality research and engaging external partners, you will bring curiosity, critical thinking and a deep interest in the relationship between storytelling, popular culture and
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and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.