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<p>Dear colleagues,</p>
<p>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. </p>
<p>We are searching for candidates who have:</p>
<p> - Fundamental understanding or experience in Koopman and/or
Mori-Zwanzig formalism</p>
<p> - Fundamental understanding or experience in Kalman and/or
Bayesian filters.</p>
<p> - 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.</p>
<p> - Excellent programming skills and experience with modern
ML libraries (e.g., PyTorch, JAX, TensorFlow) and tools beyond
online courses or certifications.</p>
<p> - Ability to work independently and in a collaborative and
multi-disciplinary scientific environment with tight
deadlines.</p>
<p>This position does not require a security clearance.
Selected candidates will be subject to drug testing and other
pre-employment background checks.</p>
<p> 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. </p>
<p>For more information, please visit jobs.lanl.gov, and search
for vacancy IRC133840. </p>
<p>For questions about this position, please contact Yen Ting
Lin (<a href="mailto:yentingl@lanl.gov" title="mailto:yentingl@lanl.gov" class="moz-txt-link-freetext">yentingl@lanl.gov</a>)</p>
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