Location: Los Alamos, NM
Cap-Exempt H-1B Position — No lottery required
**What You Will Do** **The Materials Physics and Application Division -Center for Integrated Nanotechnologies (MPA-CINT) and Theoretical Division-Physics and Chemistry of Materials (T-1) at Los Alamos National Laboratory seeks a highly motivated post-doctoral candidate, in the areas of Computer Vision (CV), Artificial Intelligence (AI), and materials modeling, with an emphasis on characterizing micrographs/radiographs using computer vision and AI. Successful applicants will work in Group CINT of the Materials Physics and Application Division and collaborate with a larger group of scientists and postdocs from across organizations.** **This postdoctoral position is part of a larger project that is focused on developing and utilizing computer vision models for characterizing materials micrographs/radiographs and classify defects, interfaces, and joints. To connect imaging-derived features to materials structure-property relationships. The successful applicant will be expected to integrate with the project team broadly and coordinate with experimentalists. The results are expected to be published in peer-reviewed journals and presented at prominent conferences. We provide unique opportunities for cross disciplinary collaborations, scientific workshop organization, and conference attendance. Outstanding applicants may be nominated for prestigious LANL-funded fellowships, enabling the pursuit of independent research.** **What You Need** Minimum Job Requirements: - Demonstrated expertise in one or more of the following: - Advanced CV/AI for materials micrographs, including segmentation/classification of defects, interfaces, and joints (e.g., U-Net, Mask R-CNN, Vision Transformers). - Experience in training Artificial Intelligence models with PyTorch, TensorFlow - Materials modeling/atomistic simulation experience relevant to mechanics (e.g., MD/DFT, microstructure-property relationships, defect physics, or related modeling approaches). - Strong programming skills in Python