Postdoctoral Appointee - Federated Learning and Distributed Computing
- Argonne National Laboratory
- Location: Lemont, USA
- Job Number: 7264179 (Ref #: 418597)
- Posting Date: 3 months ago
Job Description
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.
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.
Key Responsibilities:
- Develop and implement techniques for integrating APPFL and the Globus suite into Nexus workflows.
- Collaborate closely with scientists from MCS, DSL, and ALCF to understand their needs and develop customized solutions.
- Create new methods and algorithms for federated learning that can be applied across multiple DOE user facilities.
- Contribute to the expansion of the federated learning and distributed computing capabilities, enhancing its services and usability.
- Participate in interdisciplinary research projects and contribute to scientific publications.
- Present research findings at national and international conferences.
Position Requirements
Required skills and qualifications:
- 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
- Skilled in federated learning, distributed computing, or machine learning.
- Experience with software development and integration, particularly in high-performance computing environments.
- Proficiency in Python.
- Excellent communication and teamwork skills, with the ability to work in a multidisciplinary environment.
Preferred skills and qualifications:
- Experience with scientific computing workflows and data management.
- Knowledge of DOE user facilities and their computational needs.
- Demonstrated ability to conduct independent research and contribute to collaborative projects.
- High performance computing (HPC) experience.
Job Family
Postdoctoral FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
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