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<p>Come join one of the most innovative and creative
multidisciplinary research institutions! <br>
<br>
The Information Sciences Group (CCS-3) in the Computer,
Computational and Statistical Sciences Division at Los Alamos
National Laboratory (LANL), in collaboration with the Physics and
Chemistry of Materials Group (T-1) and Theoretical Biology and
Biophysics Group (T-6) in the Theoretical Division, are recruiting
a highly motivated post-doctoral research associate. We have
secure funds for supporting two 2-year postdoctoral research
associates focusing on<br>
<br>
</p>
<ul>
<li>(IRC92672) Multiscale modeling for Discrete Dislocation
Dynamics: The successful candidate will be part of a
multi-divisional team working on the development of an
integrated and automated multiscale simulation capability,
driven by exascale computing, data-driven methods including but
not limited to machine learning and rigorous uncertainty
quantification. The successful candidate will be expected to
work in an interdisciplinary team environment and interact with
scientists working in material science, data science,
statistical physics, machine learning, in different
organizations of the Laboratory (CCS-3/T-1/CCS-7). The candidate
will develop and implement data-driven and dynamical
coarse-graining methods for upscaling/construction of mesoscopic
models that are capable of capturing long-time behavior using
atomistic simulation data. The candidate will also develop
integrated uncertainty quantification methods for dynamically
downscaling/folding back to atomistic simulations when the
quality of mesoscopic models deteriorates in the dynamical
simulations.</li>
</ul>
<br>
<ul>
<li>(IRC88412) <span>The successful candidate will develop and
implement cutting-edge statistical inference methods and apply
them to biological data, with an emphasis on applications in
epidemiological forecasting. The fundamental research will
involve the development of efficient and robust methods for
high-dimensional computational Bayesian analysis, leveraging
both adjoint sensitivity methods and neural computation. The
successful candidate will be expected to work within an
interdisciplinary team environment and interact with
scientists working in data science, statistical physics,
machine learning, theoretical and experimental biophysics, in
different organizations of the Laboratory (T-6: Theoretical
Biology and Biophysics Group; CCS-3: Information Sciences
Group; T-5: Applied Mathematics and Plasma Physics).</span></li>
</ul>
<br>
For details, please visit jobs.lanl.gov and search for the unique
IRC identifiers.<br>
<br>
Potential candidates can make informal inquiries to Yen Ting Lin
(<a class="moz-txt-link-abbreviated" href="mailto:yentingl@lanl.gov">yentingl@lanl.gov</a>). <br>
<br>
<p><span>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 </span><a target="_blank">applyhelp@lanl.gov</a><span> or
call 1-505-665-4444 option 1.</span></p>
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