High Performance Computing (HPC) Analyst - Post Doctoral Fellow

Job Description
In partnership with Nova Scotia Post-Secondary Institutions, Federal and Provincial governments, IBM is supporting collaborative research projects that leverage high performance computing and data science to drive commercial outcomes related to industries of the ocean economy. This partnership will support multiple parallel projects involving a variety of stakeholders to support economic development and help position Nova Scotia and Atlantic Canada as a world class center for ocean based commercialization and research. We are now hiring to support these projects.

Specialist - IBM DeepSense high performance computing platform based in Halifax at Dalhousie University.

This role is for a cross-team specialist who will support the Platform. The selected candidate will be part of a team based at Dalhousie and provide parallel programming support to multiple research teams. The research teams include faculty, postdoctoral fellows, graduate students and other industry partners. The high-performance computing platform is comprised of powerful server-class systems for big-data analytics and state of the art GPGPU compute notes for scientific computing and machine learning. IBM is providing many of it's analytics and data science software suites including IBM DB2, IBM Streams and Spectrum Conductor as part of the platform. This role is pivotal to advising, building and optimizing applications to leverage massively parallel platforms and drive innovation to support economic growth and prosperity for the industries of the ocean.

Job Responsibilities:

• Assist research teams in making effective use of the DeepSense computing platform and associated technologies including IBM big data analytics software and open source technologies.
• Develop and optimize analytic models and associated software assets to support collaborative projects between research and industry partners
• Parallel-programming assistance on IBM Power servers including GPU accelerated systems.
• Install, upgrade and lead day-to-day operations for scientific infrastructure including software packages, libraries, compilers, visualization software etc
• promote, advise and teach mini-courses on scientific computing, parallel programming and data science.

You would have experience with machine/deep learning and associated software ( e.g. Tensorflow, Caffe, Torch, etc.) Experience with big-data and data science software frameworks such as Hadoop/Spark ecosystem and SQL/NoSQL databases.

Required Technical and Professional Expertise

PhD in quantitative science or engineering (e.g. Computer Science, Astrophysics, Computational biology, Computational chemistry/physics) are preferred.
Minimum 3 years of experience in using and developing large-scale parallel scientific applications making use of both MPI and OpenMP for parallellization
Experience with GPU accelerators and programming in CUDA and/or OpenCL
Experience and understanding scientific numerical codes, compilers, code optimization, Linux kernel & OS
Knowledge of data science techniques and associated programming languages such as Python, R, Java and Scala
Proven communication skills, both written and verbal

Preferred Tech and Prof Experience

Doctorate Degree in Engineering (Computer Science)
At least 6 months experience in multi-disciplinary projects where team has a diverse set of domain expertise
At least 6 months experience in working directly with clients and/or industry partners
Experience in research and development environment
Linux systems administration
Cluster management & Cluster scheduling
Parallel filesystems such as GPFS
Networking/interconnect (RoCE/IB)
MPI Programming

EO Statement
IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

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