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Postdoc in Machine Learning for Power Systems and Modeling of Cyber-Physical
Los Alamos National Security in
 
 
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Date Posted 10/12/2021
Category
Admin-Tutors and Learning Resources
Employment Type
Application Deadline Open until filled
 
 
 
 
 

What You Will Do

The successful candidate will support projects that focus on solving emerging and challenging inference and learning problems in the areas of transmission and distribution system modeling, simulation, and analysis. The candidate is expected to work in an interdisciplinary environment with significant collaborative research and to publish in peer-reviewed journals and present their research at conferences and workshops.

This position is part of LANL’s Infrastructure Science Programs, including:

-Advanced Network Science Initiative (ANSI): ANSI performs basic and applied research focused on modeling and understanding the nation's critical infrastructures, such as electric power and natural gas. Our expertise covers statistics, stochastic methods, machine learning, control theory, dynamical systems, discrete and continuous optimization, statistical physics, and graphical modeling. We ensure the scientific and technological validity of our approaches by working closely with physicists, engineers, mathematicians, statisticians, computer scientists, and economists through interdisciplinary collaborations. Our work typically requires close collaborations within and outside the Laboratory. More details on ANSI can be found at https://lanl-ansi.github.io/

-Department of Energy: The candidate will work with LANL’s applied energy program office and the U.S. Department of Energy’s Office of Electricity. Programs and projects include control and optimization of networks under uncertainty, analysis of extreme events, design of resilient networks, analysis of interdependent networks, and machine learning for infrastructure systems.

What You Need

Minimum Job Requirements:

  • Demonstrated ability to work independently and collaboratively on team

  • Strong analytical and mathematical skills

  • Experience in statistics, statistical physics, and quantum physics

  • Scientific/numerical programming experience in Julia (preferred), Python, MATLAB, or C++ Good interpersonal, written, and oral communication skills.

Education/Experience :

A Ph.D. in a relevant science or engineering field (physics, electrical engineering, computer science, computer engineering, applied mathematics, operations research, applied mathematics, statistical physics or a related field).

Desired Qualifications:

Technical experience or expertise in one or more of the following areas:

  • Scientific/numerical programming experience in Julia

  • Experience in solving practical science and engineering problems, especially in the context of infrastructure networks

  • Background in applied mathematics disciplines such as optimization, control theory, machine learning, and statistical physics

  • Publications in peer reviewed scientific journals

Location: This position will be physically located in Los Alamos, New Mexico.

COVID Vaccine :

The COVID vaccine is mandatory for all Laboratory employees, on-site contractors, and on-site subcontractors unless granted an accommodation under applicable state or federal law. This requirement will apply to those working on-site, those teleworking, and all new hires.

Note to Applicants: Along with your resume and transcript, please include 3 references and a detailed cover letter describing your research interests and explaining how you meet each of the minimum requirements and any of the desired qualifications.

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 (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.

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.

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. Appointment Type

Postdoc

Postdoc

Contact Details
Contact Name
Email
Work Telephone
Vacancy Name: IRC90934
Organization Name T-4/Phys Of Condensed Matter & Complex Sys
Minimum Salary
Maximum Salary
Req ID: IRC90934
Category: Postdoc


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