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Job Location: Los Alamos, NM
Job Detail:
We are looking for candidates to join our team in Los Alamos National Laboratory’s Theoretical Division (T-Division). T-Division provides a unique and engaging environment that fosters multi-disciplinary collaborations to support the laboratory’s strategic basic research and national security missions. The Applied Mathematics and Plasma Physics (T-5) group within T-Division conducts research across a wide variety of topics including fusion science, space plasma science, numerical analysis, computational imaging, optimization, environmental sciences, and the modeling of critical infrastructure (gas pipelines and electrical grids). These application areas are tied together by research in novel algorithms, numerical methods, artificial intelligence, and machine learning.
The Applied Mathematics and Plasma Physics group is seeking qualified candidates to hire as graduate students to work within our research teams. This is an educational opportunity for the selected candidate to contribute to cutting-edge scientific research and to expand their skill set. This position is open to students who will be actively enrolled in a graduate program throughout the appointment. Summer positions are also open to graduating undergraduates who have been accepted to graduate programs in Spring of 2024. Selected students must have and maintain a minimum GPA of 3.2 out of 4.0 on their graduate work. Most students participate in the program for a 10-12 week period over the summer. However, applications are open for students to participate in spring and fall semester or year-round. Selected candidates will be paired with a mentoring team and will work closely with one of the scientific teams within T-5.
What You Need
Minimum Job Requirements:
- Enrollment in a Master’s or Doctoral Degree in Physics, Applied Mathematics, Engineering, or closely related field
- A minimum 3.2 out of 4.0 cumulative GPA on graduate course work
- Ability to work within a collaborative team environment
- Desire to learn new things and openness to work on different projects
Education/Experience: Current enrollment graduate degree in applied mathematics, engineering, physics, or a related field
Desired Qualifications:
- Ability to independently learn the usage of scientific software packages and how to perform analytical calculations
- Experience in one or more of the following topical areas:
o Applied Scientific Computing Research
o Numerical analysis
o Algorithm development
o Linear and nonlinear partial differential equations
o Scientific and Computational Imaging
o Network analysis, optimization, and control
o Critical Infrastructure
o Integrated plasma simulation
o Theoretical modeling applied to magnetic fusion
o Inertial confinement fusion
o Astrophysical and space plasmas
o Dense, strongly coupled and quantum plasma
o Artificial Intelligence or Machine Learning
Work Environment:
Work Location: The work location for this position is onsite and located in Los Alamos, NM
Note to Applicants:
- Contact t5jobs@lanl.gov or Melanie Vigil (mlvigil@lanl.gov) for more details about this position
Include a CV with contact for 2 references and a one-page cover letter highlighting your qualifications and research interests. In your letter please clearly highlight one to three of the topical areas listed above in which you are interested in conducting research.
- For full consideration for summer 2024 positions please apply by January 15th, 2024.
- Salaries range from $32.43/hr to $40.24/hr depending on education completed. Full salary information can be found at: https://www.lanl.gov/careers/career-options/student-internships/_assets/docs/Student%20Salary%20Structure%202023.pdf
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 maternity and parental leave
- Award-winning 401(k) (6% matching plus 3.5% annually)
- Learning opportunities and tuition assistance
- Flexible schedules and time off (paid sick, vacation, 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 the 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.
No Clearance: Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.
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 and supports a diverse and inclusive workforce. All employment practices are based on qualification and merit, without regard to race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation or preference, 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 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 1-505-665-4444 option 1.
Instructions on How to Activate/Create a LANL Jobs Account:
Follow the instructions below if you have ever had an employee Z number, been a contractor, or received Los Alamos Lab insurance coverage to activate your account:
- Select the Click Here button if you have been employed with the Lab or received insurance coverage.
- Please enter only your first and last name and current email address (an email with your validation code will be sent to you) to activate the account currently in our system.
- Enter your validation code as described in the email you receive and complete the 3-page registration form. Your account is now active, and you can apply for jobs or save to your basket. Important: Enter the validation code within 15 days to activate your account or your account will be deactivated
Follow the instructions below if you if you have never been employed with the Lab or received insurance coverage to create an account:
- Select the Register button if you have never been employed with the Lab or received insurance coverage to Create an Account.
- From here, you will establish an account with username and password
How to Apply: How to Apply: Login to Your Account to Complete the Application Process
- Click the Vacancy Name number (in blue) to view any job’s details.
- Click Apply or Add to Basket to apply later. Tip: To apply for a job or save your basket, you must have a LANL jobs account.
If you experience any technical issues, please email applyhelp@lanl.gov for assistance.
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