Graduate Student Intern in ML for materials modeling

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 6,000 - 9,000; Full time

Benefits offered by this job

Medical and dental insurance

Parental leave

401(k) with company match

Tuition assistance

Flexible schedules

Onsite gyms

Relocation assistance

Job summary

Los Alamos National Laboratory in Los Alamos, NM invites applications for a 10–12 week Graduate Student Internship in computational material science. The role focuses on developing data generation methods for robust ML models and transferable interatomic potentials across diverse materials and conditions.

You will design and evaluate algorithms, implement them in Python or Julia, and work toward publication-grade results while collaborating with the AI4ND group in the X-Computational Physics

Qualifications

• Must be enrolled in a graduate program in a science or math field.
• Strong background in ML for materials science and interatomic potentials.
• Experience with Python or Julia and differentiable programming.

Responsibilities

• Characterize training dataset diversity using multiple metrics.
• Develop data generation algorithms to maximize diversity.
• Train ML models on generated datasets.
• Extensively validate model accuracy and transferability.
• Implement methods in Python or Julia.

Skills

ML surrogate models

Interatomic potentials for materials

Python

Julia

Differentiable programming

Communication skills

Independent & collaborative research

Education

Graduate program in Physics, Chemistry, Materials Science, Applied Math or related

GPA >= 3.2 (4.0 scale)

Tools

Density Functional Theory

Modular scientific software

Job description

What You Will Do

Staff in the AI4ND group in the X-Computational Physics Division at Los Alamos National Laboratory are seeking a candidate for a 10 to 12 weeks Graduate Student Internship focusing on computational material science, with an emphasis on the development of novel data generation methods for training robust ML models. The position supports several research projects focused on the development of highly transferable Machine Learned Interatomic Potentials and in their application to a broad range of physical systems including low-dimensional systems and extreme conditions. The research will involve the design, implementation, and evaluation of advanced computational algorithms to:

• Characterize the diversity of training datasets along several metrics
• Develop training data generation algorithm to maximize diversity along these metrics
• Train ML models to generated datasets
• Extensively validate the models for accuracy and transferability

The student will implement the developed methods in Python or Julia.

The ideal candidate will be pursuing the above topic for their own research in their university.

What You Need

• Familiarity with modern ML approaches to surrogate model design and training
• Demonstrated expertise with the development and use of ML interatomic potentials for materials
• Strong programming experience with differentiable programming frameworks (Python, Julia)
• Strong written and oral communication skills
• Demonstrated ability to conduct independent and collaborative research

Minimum Job Requirements:

• Familiarity with modern ML approaches to surrogate model design and training
• Demonstrated expertise with the development and use of ML interatomic potentials for materials
• Strong programming experience with differentiable programming frameworks (Python, Julia)
• Strong written and oral communication skills
• Demonstrated ability to conduct independent and collaborative research

Education/Experience

• Must be enrolled (6 semester credit hours or full-time equivalent) in an accredited, degree-granting graduate level program (or international equivalent) in Physics, Chemistry, Materials Science, Applied Mathematics, or related fields
• Must currently have and maintain a cumulative GPA of at least 3.2 on a 4.0 scale (or equivalent).

Desired Qualifications:

• Experience carrying out and analyzing atomistic simulation of materials using molecular dynamics
• Experience with dataset generation using Density Functional Theory
• Demonstrated ability to design and implement modular scientific software

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.

Note to Applicants:

Due to federal restrictions contained in the current National Defense Authorization Act, citizens of the People's Republic of China-including the special administrative regions of Hong Kong and Macau-as well as citizens of the Islamic Republic of Iran, the Democratic People's Republic of Korea (North Korea), and the Russian Federation, who are not Lawful Permanent Residents ("green card" holders) are prohibited from accessing facilities that support the mission, functions, and operations of national security laboratories and nuclear weapons production facilities, which includes Los Alamos National Laboratory.

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

Required Application Materials

• Current resume
• Current official transcripts
• Personal statement of interest (not to exceed one page)

Where You Will Work

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)

Additional Details

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

New-Employment Drug Test:

The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing. Although New Mexico and other states have legalized the use of marijuana, use and possession of marijuana remain illegal under federal law. A positive drug test for marijuana will result in termination of employment, even if the use was pre-offer.

Internal Applicants:

Regular appointment employees who have served the required period of continuous service in their current position are eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the required period of continuous service, they may only apply for Laboratory jobs with the documented approval of their Division Leader. Please refer to Policy Policy P701 for applicant eligibility requirements.

Equal Opportunity:

Los Alamos National Laboratory is an equal opportunity employer. All employment practices are based on qualification and merit, without regard to protected categories such as race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal, state, and local laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to [e-mail] or call (505)-664-6947.