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environment. The successful candidate will develop and apply advanced machine learning techniques—including multimodal AI, computer vision, and large language models—to complex scientific and engineering
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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comparative research across Mojo, Julia, Rust, and vendor toolchains. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, or related field. Experience with LLMs or agentic AI frameworks
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strengths in any of these areas — quantitative imaging, modeling/transport science, machine learning, or scientific programming — are encouraged to apply. Major Duties/Responsibilities: Lead energy‑storage
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Requisition Id 15885 Overview: We are seeking a Postdoctoral Research Associate – Simulation and Machine Learning for Composite Manufacturing who will focus on developing physics-based simulation
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
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more than 60 years of achievement in local, regional, national, and international environmental research. Our vision is to expand scientific knowledge and develop innovative strategies and technologies
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL