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Physics Informed Machine Learning Jobs in New Mexico

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Physics Informed Machine Learning information

What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.
What are popular job titles related to Physics Informed Machine Learning jobs in New Mexico? For Physics Informed Machine Learning jobs in New Mexico, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in New Mexico look for? The top searched job categories for Physics Informed Machine Learning jobs in New Mexico are:
Data-driven and Machine Learning Postdoctoral Research Associate

Data-driven and Machine Learning Postdoctoral Research Associate

Los Alamos National Laboratory

Los Alamos, NM • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 3 days ago


Los Alamos National Laboratory rating

9.2

Company rating: 9.2 out of 10

Based on 32 frontline employees who took The Breakroom Quiz

7th of 103 rated laboratories


Job description

Description
Job Title Data-driven and Machine Learning Postdoctoral Research Associate
Location Los Alamos, NM, US
Organization Name Computational Physics and Methods (CAI-2)
Minimum Salary
Maximum Salary
What You Will Do
The Computational and Physics Methods Group (CAI-2) in the Computing and Artificial Intelligence Division at Los Alamos National Laboratory is seeking a skilled and driven researcher for a postdoctoral position at the intersection of applied mathematics, data-driven modeling of dynamical systems, and machine learning.
The successful candidate will join a multidisciplinary team of mathematicians, physicists, and machine learning researchers advancing AI-enabled modeling of complex dynamical systems. Leveraging transfer operator theory (e.g., Koopman and Perron-Frobenius methods), the postdoc will develop novel learning architectures that both respect physical constraints and help discover underlying structure from data. Core activities will span method development, theoretical analysis, and empirical validation at scale on benchmark and mission-relevant datasets. The position offers exposure to multiple application domains (e.g., wildfire, ocean, and space weather), as well as opportunities for cross-disciplinary collaboration, scientific workshop organization, and conference participation.
What You Need
Minimum Job Requirements:
  • Experience in data-driven and/or ML methods for dynamical systems, as evidenced through a strong scientific record of peer-reviewed publications and presentations.
  • Fundamental understanding of the Koopman and/or Perron-Frobenius Operators.
  • Excellent scientific programming skills with demonstrated, hands-on experience (beyond online courses/certifications) using modern ML libraries and tools-e.g., PyTorch and/or JAX-as well as high-level languages such as Python (including NumPy/SciPy).
  • Strong mathematical training in at least one relevant field (e.g., functional analysis/operator theory, probability/stochastic processes, numerical analysis/scientific computing, and/or optimization/ML theory).
  • Ability to work both independently and collaboratively in an interdisciplinary environment.
  • Ability to communicate technical results clearly in writing and presentations.
  • Demonstrated creativity and interest in developing new research directions and original methodologies.

Education/Experience : PhD in Applied Mathematics, Computational or Statistical Physics, Applied Statistics, Computer Science, or a related field completed within the last 5 years or to be completed soon.
Desired Qualifications:
  • Prior research experience directly involving the Koopman and/or Perron-Frobenius operators.
  • Prior research experience developing and/or implementing neural operators.
  • Strong background in functional analysis/operator theory, including spectral theory, reproducing kernel Hilbert space methods, and the approximation of infinite-dimensional systems by finite-dimensional models.
  • Experience with probabilistic modeling and uncertainty quantification (e.g., Bayesian deep learning, generative models, variational inference, ensembles, probabilistic scoring rules).
  • Experience developing novel neural network architectures (e.g., customized loss functions, complex network topologies, constrained or structure-preserving architectures).
  • Experience working with large numerical simulations or high-dimensional datasets and familiarity with high-performance computing environments (e.g., clusters, GPUs, job schedulers).

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.
For full consideration please include:
• A comprehensive CV with publication list
• A cover letter describing your qualifications and how you meet the job requirements
• Contact information for at least three professional references.
For questions about this position contact: Derek DeSantis (ddesantis@lanl.gov) or Yen Ting Lin (yentingl@lanl.gov).
For more information, visit LANL career page: https://www.lanl.gov/careers/index.php .
Outstanding candidates may be considered for a Director's Postdoc Fellowship. For more information about the Postdoc Program, go to: https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php
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.
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.
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 Full Time
Appointment Type Postdoc
Postdoc
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