What You Will Do

The Group of Physics of Condensed Matter and Complex Systems in the Theoretical Division of Los Alamos National Laboratory has an immediate postdoctoral position available for a talented and motivated researcher interested in at least one of the following areas:

(i)               machine learning modeling applied to materials science,

(ii)              generative AI for designing and discovering new molecular systems,

(iii)            density functional theory calculations and data science analysis for materials discovery.

The successful candidate will have the opportunity to engage with a dynamic, multidisciplinary experimental and theoretical research team that bridges fundamental science with applications and materials development, characterization, and testing.

Education/Experience: Ph.D. in either theoretical/computational Physics, Chemistry, Computer Science or related area completed within the last 5 years or to be completed soon.

Clearance: Q Clearence

The selected candidate will be required to access Export Controlled Information, which is limited to U.S. permanent residents and citizens.

The successful candidate must have:

• Demonstrated experience of strong programming skills using high-level languages such as Python.
• Strong knowledge and experience, as evidenced through scientific record of peer-reviewed publications and presentations, in computer science, machine learning methods and/or applied generative AI. Preference will be given to candidates that also have experience in applying data science/ML/AI for materials discovery and design.
• The ability to work creatively and independently as well as learn and collaborate with experts and contribute to a diverse multi-disciplinary team.
• Strong communication and language skills, as evidenced by publications, cover letter, and interview.

Desired Qualifications:

In addition to the minimum requirements, the following qualifications are desired, and preference will be given to candidates satisfying (at least one) of them:

• Experience with AI for science, science-informed machine learning, inverse problems, uncertainty quantification, transformer architectures, foundation models, or surrogate modeling, beyond online courses and certifications.
• Experience with high-performance computing, GPU programming, parallel programming, and related paradigms.
• Experience with modern machine-learning libraries and tools (e.g., Scikit-Learn, PyTorch, TensorFlow), especially for training and deploying generative AI models.
• Knowledge of modern electronic structure theory codes (e.g., Gaussian16) and their application for modeling materials properties.
• Experience with high-throughput frameworks (e.g., pyiron, aflow, pymatgen, etc) and/or cheminformatics tools (e.g., RDKit)
• Willingness to work productively on multiple projects.

Contact one of the project members with questions about this position: Wilton Kort-Kamp (kortkamp@lanl.gov),  Cristina Garcia-Cardona (cgarciac@lanl.gov), or Ivana (née Matanovic) Gonzales (ivana@lanl.gov).

To start your application, please upload

• CV with publication list
• Cover letter describing your qualifications and how they meet the job requirements
• A brief statement of research interests (maximum of one page) with
• The names of at least three references

Candidates may be considered for a fellowship supported by the Center for Nonlinear Studies (CNLS). Outstanding candidates may be considered for a Director's Postdoc Fellowship or for the prestigious Richard P. Feynman, Darleane Christian Hoffman, J. Robert Oppenheimer, or Frederick Reines Distinguished Postdoc Fellowships.

Additional information on salary ranges and other benefits can be found online at https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php?source=globalheadernav .

For more information about the Postdoc Program, go to https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php

Where You Will Work

Clearance:  Q (Position will be cleared to this level).  Selected applicants will be subject to a background investigation conducted by or on behalf of the Federal Government, and must meet eligibility requirements* for access to classified matter. This position requires a Q clearance. and obtaining such clearance requires US Citizenship except in extremely rare circumstances. Dependent upon the position, additional authorization to access classified information may be required, which may or may not be available to dual citizens.  Receipt of a Q clearance and additional access authorization ultimately is a decision of the Federal Government and not of Triad.

*Eligibility requirements:  To obtain a clearance, an individual must be at least 18 years of age; U.S. citizenship is required except in very limited circumstances. See DOE Order 472.2 for additional information.