[Job-offers-cs] Postdoc at Argonne National Laboratory - Federated Learning for Foundation Models

[Pekka Orponen]

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.

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.

*Position Requirements*

_Required skills and qualifications:_

  * Ph.D. (completed within the past 0-5 years) in computer science,
    statistics, data science, applied mathematics, operational research,
    or a related field.
  * Proficiency in coding with Python and experience in C, C++, or other
    comparable languages.
  * Strong background in machine learning techniques and familiarity
    with ML frameworks such as PyTorch, Jax, or TensorFlow.
  * Proven ability to collaborate effectively with scientists,
    divisions, and external institutions, including universities and
    national laboratories.
  * Excellent oral and written communication skills for engaging with
    all levels of the organization.
  * Ability to model Argonne's core values of impact, safety, respect,
    impact, and teamwork.

_Preferred skills and qualifications:_

  * Experience with federated learning, particularly in the context of
    training or deploying foundation models.
  * Expertise in managing large-scale training datasets using
    GPU-enabled computing.
  * Familiarity with privacy-preserving machine learning techniques.
  * Experience with distributed computing, scaling machine learning
    models, or handling heterogeneous datasets.
  * Knowledge of continual learning frameworks and strategies.
  * A strong foundation in statistical methods, optimization, or game
    theory is a plus.

Postdoctoral Appointee - Federated Learning for Foundation Models 
<https://argonne.wd1.myworkdayjobs.com/en-US/Argonne_Careers/job/Postdoctoral-Appointee---Federated-Learning-for-Foundation-Models_419053>


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Kibaek Kim, PhD
Computational Mathematician
Mathematics and Computer Science Division
Argonne National Laboratory, USA
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