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<p>The Mathematics and Computer Science (MCS) Division at Argonne
National Laboratory invites candidates to apply for a postdoctoral
position in the areas of federated learning and distributed
computing.</p>
<p>This postdoctoral appointee will work on integrating Argonne's
federated learning tool (APPFL) and the Globus suite into the
Argonne Leadership Computing Facility (ALCF)'s Nexus workflows,
supporting the Department of Energy (DOE) user facilities. This
position offers a unique opportunity to collaborate with leading
scientists from MCS, DSL, and ALCF, and to develop innovative
techniques for federated learning and services across multiple
facilities.</p>
<p>Key Responsibilities:</p>
<ul>
<li>Develop and implement techniques for integrating APPFL and the
Globus suite into Nexus workflows.</li>
<li>Collaborate closely with scientists from MCS, DSL, and ALCF to
understand their needs and develop customized solutions.</li>
<li>Create new methods and algorithms for federated learning that
can be applied across multiple DOE user facilities.</li>
<li>Contribute to the expansion of the federated learning and
distributed computing capabilities, enhancing its services and
usability.</li>
<li>Participate in interdisciplinary research projects and
contribute to scientific publications.</li>
<li>Present research findings at national and international
conferences.</li>
</ul>
<p><b>Position Requirements</b></p>
<p>Required skills and qualifications:</p>
<ul>
<li>Recently or soon-to-be completed Ph.D. (within the last 0-5
years) in Computer Science, Applied Mathematics, Electrical
Engineering, or a related field</li>
<li>Skilled in federated learning, distributed computing, or
machine learning.</li>
<li>Experience with software development and integration,
particularly in high-performance computing environments.</li>
<li>Proficiency in Python.</li>
<li>Excellent communication and teamwork skills, with the ability
to work in a multidisciplinary environment.</li>
</ul>
<p>Preferred skills and qualifications:</p>
<ul>
<li>Experience with scientific computing workflows and data
management.</li>
<li>Knowledge of DOE user facilities and their computational
needs.</li>
<li>Demonstrated ability to conduct independent research and
contribute to collaborative projects.</li>
<li>High performance computing (HPC) experience.</li>
</ul>
<p></p>
<p>Link to apply: <a href="https://argonne.wd1.myworkdayjobs.com/en-US/Argonne_Careers/job/Postdoctoral-Appointee---Federated-Learning-and-Distributed-Computing_418597" title="Unmangled Microsoft Safelink">Postdoctoral Appointee -
Federated Learning and Distributed Computing</a></p>
<p><br>
</p>
------------------------------<br>
Kibaek Kim, PhD<br>
Computational Mathematician<br>
Mathematics and Computer Science Division<br>
Argonne National Laboratory, USA<br>
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