Postdoctoral Research Associate in Scalable Machine Learning and Data Analysis in High Performance Computing Systems

Company Name:
Oak Ridge National Laboratory
The Computational Data Analytics Group in the Computational Sciences and Engineering Division at Oak Ridge National Laboratory seeks qualified postdoctoral candidates interested in conducting research on machine learning algorithms that scale on world-class leadership storage and computing systems.
The overarching goal of our work will be to create, study and optimize data analysis workflows for future HPC and data centers. We will investigate disruptive disk technologies and non-volatile memory-based storage systems that can interact with each other towards developing workflow-aware storage. We are seeking a candidate with strong expertise in machine learning algorithms with experience in file and storage systems and programming models such as Map Reduce, Bulk-synchronous parallel etc. to investigate, and build smarter storage system solutions in support of next-generation data analysis.
Job Duties and Responsibilities:
The selected candidate will actively participate in designing and building new smart storage solutions based on emerging storage and memory technologies, with an eye towards supporting future end-to-end workflows for the diverse scientific data collected at the lab. In addition, the selected candidate will conduct research and report results in open literature journals, technical reports, and at relevant conferences. Occasional travel is required.

Minimum Qualifications Required:

A Ph.D. in Computer Science or other closely related discipline is required.
Experience with programming languages: C, C++, Python, etc.
Ability to articulate research and development results in scientific publications.
A proven publication track record is also required.
Good oral and written communication skills.

Preferred Qualifications:
A Ph.D. in Computer Science with a specialization in machine learning, storage systems design, distributed storage systems, and non-volatile memory devices is preferred.

Additional preferred skills include:
A deep understanding of file and storage systems concepts and/or machine learning algorithms (supervised, unsupervised and semi-supervised methods)
Experience with file systems (e.g., ext2, Lustre, GPFS, Ceph) development
Experience with non-volatile memory/shared memory/distributed memory architectures.
Experience with distributed storage systems development.
An understanding of workflow schedulers.
Experience developing software for HPC environments - shared-memory, shared-nothing and shared-storage and distributed systems.
Provenance management.
Experience with multi-threaded programming.
Experience using shared memory.
Ability to work in uncharted territory and drive an idea from conception to implementation.

Additional Information:
Applicants cannot have received their PhD more than five years prior to the date of application and must complete all degree requirements before starting their appointment. Certain exceptions may be considered. This appointment will initially be for 24 months with a possibility of an extension of up to 12 months. Initial appointments and extensions are subject to performance and availability of funding.

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