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<p>The Mathematics and Computer Science Division (MCS) at Argonne
National Laboratory is seeking a Postdoctoral Appointee to conduct
cutting-edge research in machine learning, with a focus on the
efficient training and deployment of foundation models in a
federated learning framework. The Postdoctoral Appointee will work
on the development and optimization of federated learning
techniques to enable the training of large-scale foundation models
across distributed clients, addressing key challenges such as data
heterogeneity and communication efficiency.</p>
<p></p>
<p>The appointee will contribute to the conceptual framework,
design, and implementation of federated learning architectures,
with a particular emphasis on improving model performance, scaling
across distributed systems, and ensuring privacy and security in
data handling.</p>
<p></p>
<p></p>
<p><b>Position Requirements</b></p>
<p></p>
<p><u>Required skills and qualifications:</u></p>
<ul>
<li>Ph.D. (completed within the past 0-5 years) in computer
science, statistics, data science, applied mathematics,
operational research, or a related field.</li>
<li>Proficiency in coding with Python and experience in C, C++, or
other comparable languages.</li>
<li>Strong background in machine learning techniques and
familiarity with ML frameworks such as PyTorch, Jax, or
TensorFlow.</li>
<li>Proven ability to collaborate effectively with scientists,
divisions, and external institutions, including universities and
national laboratories.</li>
<li>Excellent oral and written communication skills for engaging
with all levels of the organization.</li>
<li>Ability to model Argonne's core values of impact, safety,
respect, impact, and teamwork.</li>
</ul>
<p></p>
<p><u>Preferred skills and qualifications:</u></p>
<ul>
<li>Experience with federated learning, particularly in the
context of training or deploying foundation models.</li>
<li>Expertise in managing large-scale training datasets using
GPU-enabled computing.</li>
<li>Familiarity with privacy-preserving machine learning
techniques.</li>
<li>Experience with distributed computing, scaling machine
learning models, or handling heterogeneous datasets.</li>
<li>Knowledge of continual learning frameworks and strategies.</li>
<li>A strong foundation in statistical methods, optimization, or
game theory is a plus.</li>
</ul>
<p></p>
<p><a href="https://argonne.wd1.myworkdayjobs.com/en-US/Argonne_Careers/job/Postdoctoral-Appointee---Federated-Learning-for-Foundation-Models_419053" title="Unmangled Microsoft Safelink">Postdoctoral Appointee -
Federated Learning for Foundation Models</a></p>
<p><br>
</p>
<p>------------------------------<br>
Kibaek Kim, PhD<br>
Computational Mathematician<br>
Mathematics and Computer Science Division<br>
Argonne National Laboratory, USA<br>
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