The Advanced Research in Cyber Systems (A-4) group is seeking a highly motivated research assistant with experience in machine learning, statistics, and mathematical modeling. Working as a member of an innovative, fast-paced team, you will tackle challenges related to information integrity and model robustness. You will be responsible for independent research, and you will be expected to present your results to technical peers, upper management, and sponsors.
When submitting your application electronically to this vacancy, please submit the following:
No Clearance: Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.
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.
Internal Applicants: Please refer to Laboratory policy P701 for applicant eligibility.
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 regards 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 firstname.lastname@example.org or call 1-505-665-4444 option 1.
Where You Will Work
Located in northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. LANL enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
The Analytics, Intelligence and Technology Division merges all-source intelligence on national security threats with cross-domain systems modeling and information synthesis and communication tools, to enable technology development and application in the national interest. Within the Division, the Advanced Research in Cyber Systems Group provides R&D solutions for a resilient and defensively agile national cyber infrastructure by ensuring communication confidentiality and integrity, anomaly detection for next-generation cyber defense, and incident response training.
Instructions on How to Activate/Create a LANL Jobs Account:
Follow the instructions below if you have had an employee Z number, been a contractor, or received Los Alamos Lab insurance coverage toactivateyour account:
Select the Click Here button if you have been employed with the Lab or received insurance coverage .
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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 tocreatean 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.
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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 email@example.com for assistance.
Appointment Type GRA
Contact Name Salyer, Reyna Linda
Req ID: IRC73323
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