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resources and collaborating across ORNL, federal agencies, and academic partners. Key Responsibilities: Develop and apply scalable molecular dynamics (MD) and multiscale simulation workflows for biomolecular
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AI/ML, with a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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intelligence, and architecture-aware algorithms that harness machines, ideas, and data to enable far reaching scientific breakthroughs. We develop computer innovations and advanced practical tools to address
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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development of novel AI algorithms, it does require strong implementation skills, sound statistical judgment, and an ability to translate methods into reliable, maintainable, and well-documented pipelines
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-sensor fusion, and propagation modeling — to develop AI-enabled detection, classification, and triangulation algorithms for critical energy infrastructure applications. This position resides within