Artificial Intelligence and Computer Vision Materials Postdoctoral Research Associate

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 classifying 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.

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, Tensor Flow
  • 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 (mainly scikit‑image, OpenCV)
• Demonstrated experience in conducting original scientific research through peer reviewed publication record.
• Excellent communication skills (both oral and written).

Desired

Qualifications:

• Solid Background in materials science and engineering.
• Experience in training AI and CV models on GPU‑accelerated super computers, with large workloads, and familiarity with PyTorch Lightning, Hugging Face, Lang Chain etc.
• Ability to adapt to new requirements for projects and be flexible enough to learn new areas of research as needed.
• Ability to work effectively as a part of a team in a multi‑disciplinary environment and interact with people with a variety of expertise.

Located in beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. Our generous benefits package includes:

• PPO or High Deductible medical insurance with the same large nationwide network
• Dental and vision insurance
• Free basic life and disability insurance
• Paid childbirth and parental leave
• Award‑winning 401(k) (6% matching plus 3.5% annually)
• Learning opportunities and tuition assistance
• Flexible schedules and time off (PTO and holidays)
• Onsite gyms and wellness programs
• Extensive relocation packages (outside a 50 mile radius)

Work Location:

The work location for this position is onsite and located in Los Alamos, NM. All work locations are at the discretion of management.

Employment Status:
Fulltime or part time options available

Directive 206.2 – Employment with Triad requires a favorable decision by NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2. This requirement applies only to citizens of the United States. Foreign nationals are subject to a similar requirement under DOE Order 142.3A.

No Clearance:
Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre‑employment background checks.

426.2A:
This position is subject to DOE Order 426.2A, Personnel Selection, Training, and…