[Job-offers-cs] Postdoc position on data-driven and machine learning for dynamical systems at Los Alamos National Lab

[Pekka Orponen]

Dear colleagues,

The Information Sciences Group (CCS-3) at the Los Alamos National 
Laboratory has immediate opening of a postdoctoral research associate 
position to work on the intersection of data-driven learning, 
uncertainty quantification, and machine learning, of dynamical systems. 
The position is fully funded for three years.

We are searching for candidates who have:

   - Fundamental understanding or experience in Koopman and/or 
Mori-Zwanzig formalism

   - Fundamental understanding or experience in Kalman and/or Bayesian 
filters.

   - Experience in data-driven and/or machine learning (ML) methods for 
dynamical systems, as evidenced through a strong scientific record of 
peer-reviewed publications and presentations.

   - Excellent programming skills and experience with modern ML 
libraries (e.g., PyTorch, JAX, TensorFlow) and tools beyond online 
courses or certifications.

   - Ability to work independently and in a collaborative and 
multi-disciplinary scientific environment with tight deadlines.

This position does not require a security clearance. Selected candidates 
will be subject to drug testing and other pre-employment background checks.

  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.

For more information, please visit jobs.lanl.gov, and search for vacancy 
IRC133840.

For questions about this position, please contact Yen Ting Lin 
(yentingl at lanl.gov)
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