AI Trustworthiness for scientific applications - Los Alamos National Laboratory
Los Alamos, NM
About the Job
What You Will Do
Are you ready to begin a career assessing AI trustworthiness for scientific applications? If so, we encourage you to learn more about our Postdoctoral Research Associate
openings in the Computational Physics: Verification & Analysis group (XCP-8) at the Los Alamos National Laboratory (LANL).
We are looking for a highly motivated post-doctoral candidate with interest in computational physics and AI for science. The selected candidate will develop and
apply methods such as explainable AI (XAI), physics-informed machine learning (PIML), verification and validation (V&V), uncertainty quantification, Bayesian optimization,
foundation models, generative deep learning, and/or computational physics simulations to improve AI trustworthiness for scientific applications. Experience in both AI
and computational science is preferred. You will have the opportunity to publish results in peer-reviewed journals, to present at top tier conferences, and the flexibility to
work on different topics.
What You Need
Minimum Job Requirements:
Numerical Methods
Experience training deep learning architectures and/or experience using computational physics models.
Research Expertise
Conducted research in one or more of the following areas: deep learning, generative AI, explainable AI (XAI), code verification and validation (V&V),
uncertainty quantification (UQ), physics-informed machine learning (PIML), and/or deep learning for scientific applications.
Programming
Familiar with Python and have experience programming in TensorFlow or PyTorch.
Journals/Conferences
Demonstrated an ability to publish research in peer-reviewed journals or conference proceedings, an ability to communicate research to an interdisciplinary audience, and be able to work on an interdisciplinary team.
Education/Experience: PhD in applied mathematics, computer science, physics, materials science, chemistry, engineering, statistics or a related STEM discipline. The candidate must be within 5 years of completion of PhD at time of being proposed for review by the committee or will have completed all PhD requirements by commencement of the appointment.
Desired Qualifications:
• Experience in the development of computational physics models.
• Experience with common generative deep learning architectures or their components (diffusion models, transformers, convolutional networks, etc.).
• Experience with explainable AI (XAI) and/or physics-informed machine learning (PIML).
• Experience using High Performance Computing clusters, including GPUs.
• Familiarity with one or more of the following physics problems: radiation transport, high-energy-density physics, equations-of-state, shock physics, high explosive modeling, material failure, material strength under high strain rates, magnetohydrodynamics, and/or materials properties in simulations.
• Experience with software engineering tools like: Git, web-based software repository systems, issue trackers, online collaboration platforms, deep learning APIs, and/or continuous integration systems.
• A strong record of peer-reviewed publications.
• Ability to work together with others on a team.
Work Environment:
Work Location: The work location for this position is onsite and located in Los Alamos, NM.
Salary: Competitive salaries are based on the date the PhD degree requirements were completed or the degree was awarded.
Starting salary for a new graduate is currently $94,500. For more information go to Postdoc Program website at https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php .
Note to Applicants:
To be considered for the position, applicants should submit a CV/resume with a publication list and the names of three references, and a cover letter addressing the minimum job requirements and any applicable desired qualifications. Regular post-doctoral appointments are for two years and are renewable for a third year. Outstanding candidates may be eligible for a LANL Director's Fellowship. For specific inquiries regarding the position, please contact Dr. Bryan Kaiser (bkaiser@lanl.gov). We will begin reviewing applications on Monday October 7th 2024.
XCP-8 is an interdisciplinary group which specializes in VVUQ and analysis of complex numerical codes applying diverse physics models (fluid dynamics, hydrodynamics, solid mechanics, material strength and damage, equation of state, reactive flow, high-energy-density physics, instabilities and turbulence, and radiation transport). AI/ML methods are applied by XCP-8 in a highly interdisciplinary manner and group members interact closely with code and physics model developers, experimentalists, and simulation end-users across a broad set of physics. XCP-8 offers an exciting, flexible, scientifically challenging work environment with many opportunities to collaborate with the broader LANL scientific community.
Learn more about XCP-8 at https://content.lanl.gov:8058/orgs/xcp/xcp-8/
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 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.
*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.
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. 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.
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, 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 1-505-664-6947 option 2 and then option 3.
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.
Are you ready to begin a career assessing AI trustworthiness for scientific applications? If so, we encourage you to learn more about our Postdoctoral Research Associate
openings in the Computational Physics: Verification & Analysis group (XCP-8) at the Los Alamos National Laboratory (LANL).
We are looking for a highly motivated post-doctoral candidate with interest in computational physics and AI for science. The selected candidate will develop and
apply methods such as explainable AI (XAI), physics-informed machine learning (PIML), verification and validation (V&V), uncertainty quantification, Bayesian optimization,
foundation models, generative deep learning, and/or computational physics simulations to improve AI trustworthiness for scientific applications. Experience in both AI
and computational science is preferred. You will have the opportunity to publish results in peer-reviewed journals, to present at top tier conferences, and the flexibility to
work on different topics.
What You Need
Minimum Job Requirements:
Numerical Methods
Experience training deep learning architectures and/or experience using computational physics models.
Research Expertise
Conducted research in one or more of the following areas: deep learning, generative AI, explainable AI (XAI), code verification and validation (V&V),
uncertainty quantification (UQ), physics-informed machine learning (PIML), and/or deep learning for scientific applications.
Programming
Familiar with Python and have experience programming in TensorFlow or PyTorch.
Journals/Conferences
Demonstrated an ability to publish research in peer-reviewed journals or conference proceedings, an ability to communicate research to an interdisciplinary audience, and be able to work on an interdisciplinary team.
Education/Experience: PhD in applied mathematics, computer science, physics, materials science, chemistry, engineering, statistics or a related STEM discipline. The candidate must be within 5 years of completion of PhD at time of being proposed for review by the committee or will have completed all PhD requirements by commencement of the appointment.
Desired Qualifications:
• Experience in the development of computational physics models.
• Experience with common generative deep learning architectures or their components (diffusion models, transformers, convolutional networks, etc.).
• Experience with explainable AI (XAI) and/or physics-informed machine learning (PIML).
• Experience using High Performance Computing clusters, including GPUs.
• Familiarity with one or more of the following physics problems: radiation transport, high-energy-density physics, equations-of-state, shock physics, high explosive modeling, material failure, material strength under high strain rates, magnetohydrodynamics, and/or materials properties in simulations.
• Experience with software engineering tools like: Git, web-based software repository systems, issue trackers, online collaboration platforms, deep learning APIs, and/or continuous integration systems.
• A strong record of peer-reviewed publications.
• Ability to work together with others on a team.
Work Environment:
Work Location: The work location for this position is onsite and located in Los Alamos, NM.
Salary: Competitive salaries are based on the date the PhD degree requirements were completed or the degree was awarded.
Starting salary for a new graduate is currently $94,500. For more information go to Postdoc Program website at https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php .
Note to Applicants:
To be considered for the position, applicants should submit a CV/resume with a publication list and the names of three references, and a cover letter addressing the minimum job requirements and any applicable desired qualifications. Regular post-doctoral appointments are for two years and are renewable for a third year. Outstanding candidates may be eligible for a LANL Director's Fellowship. For specific inquiries regarding the position, please contact Dr. Bryan Kaiser (bkaiser@lanl.gov). We will begin reviewing applications on Monday October 7th 2024.
XCP-8 is an interdisciplinary group which specializes in VVUQ and analysis of complex numerical codes applying diverse physics models (fluid dynamics, hydrodynamics, solid mechanics, material strength and damage, equation of state, reactive flow, high-energy-density physics, instabilities and turbulence, and radiation transport). AI/ML methods are applied by XCP-8 in a highly interdisciplinary manner and group members interact closely with code and physics model developers, experimentalists, and simulation end-users across a broad set of physics. XCP-8 offers an exciting, flexible, scientifically challenging work environment with many opportunities to collaborate with the broader LANL scientific community.
Learn more about XCP-8 at https://content.lanl.gov:8058/orgs/xcp/xcp-8/
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 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.
*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.
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. 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.
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, 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 1-505-664-6947 option 2 and then option 3.
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.
Source : Los Alamos National Laboratory
