Data-driven and Machine Learning Postdoctoral Research Associate

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 (https://int.lanl.gov/policy/documents/P701.pdf) for applicant eligibility 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.

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)