Quantitative Research Associate Jobs in New Mexico

Description
Job Title Undergraduate Research Associate
Location Los Alamos, NM, US
Organization Name Materials Science In Radiation & Dynamics Extremes/MST-8
Minimum Salary
Maximum Salary
What You Will Do
Staff in the Materials Science in Radiation and Dynamics Extremes Group (MST-8) in the Materials Science and Technology Division at Los Alamos National Laboratory (LANL) are seeking a candidate for the Graduate Research Assistant Program in computational materials science and data-driven experimental design. The position is in support of several research projects that address multi-scale problems relating to nuclear fuel systems, including oxide, metallic, and other non-oxide ceramics fuels as well as steel, zirconium, and silicon carbide cladding, as well as their performance in reactor systems. This position supports academic research focused on the development of advanced computational methods for adaptive experimental design in quantitative elemental imaging workflows. The research will involve the design, implementation, and evaluation of advanced computational algorithms that:

• Identify spatial and compositional patterns in multi-element mapping datasets (e.g., gradients, segregation, inclusions, clustering, interfaces, and anomalous regions);
• Recommend follow-on measurement actions to maximize information gain, including:

• Identifying regions where additional raster scans should be performed,
• Suggesting locations for higher-resolution or higher-statistics scans,
• Proposing targeted point analyses to refine compositional estimates;

• Optimize acquisition parameters (e.g., dwell time, step size, spatial resolution, and count-statistics proxies) to enhance data quality while respecting operational and instrument constraints;
• Detect and manage out-of-bounds or invalid parameter selections by incorporating safe operating limits into the decision-making framework;
• Integrate uncertainty quantification and data-quality metrics to prioritize measurements and avoid low-value or unreliable regions.

The student will implement the developed methods in Python-based, open-source software with emphasis on reproducibility, modular architecture, and transparency. The resulting codebase will be structured as a generalizable, community-accessible framework suitable for peer-reviewed publication and broad dissemination.
The ideal candidate will be pursuing the above topic for their own research in their University.
For general information to the Student Program go to https://www.lanl.gov/careers/career-options/student-internships/graduate/index.php.
What You Need
Minimum Job Requirements:

• Strong programming experience in Python, including scientific computing libraries (NumPy, SciPy, scikit-learn, PyTorch/TensorFlow, etc.);
• Experience with machine learning, AI methods, or data-driven modeling;
• Familiarity with uncertainty quantification, statistical inference, or Bayesian experimental design;
• Experience analyzing spatially resolved or imaging datasets;
• Demonstrated ability to design and implement modular, reproducible scientific software;
• Experience working with scientific instrumentation data or experimental workflows (preferred);
• Demonstrated ability to conduct independent and collaborative research;
• Strong written and oral communication skills.

Education:
Enrolled in a Ph.D. program in materials science, computer engineering, or a closely related discipline.
Notes to Applicants:

• The cover letter should address the relevant job requirements and desired skills of this position, with specific examples from your academic and research experience. Expected degree completion dates and relevant coursework should also be included.
• Evaluation of applications will commence immediately and continue until the position is filled.

Requirements for acceptance:

• Documentation of acceptance into a graduate program.
• Successfully completed a minimum of 6 graduate credit hours per semester (or the equivalent of full-time enrollment). All students MUST remain enrolled at their university while working at the Lab during the academic year.
• Have and maintain a minimum cumulative GPA of 3.5 on 4.0 scale.
• Pass a 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.
• No Clearance Required: Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.

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.
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 NNSA 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 applyhelp@lanl.gov or call (505)-664-6947.
Employment Status Casual
Appointment Type UGS
UGS
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