Job description

Requirements

  • Entry level
  • No Education
  • Salary to negotiate
  • Toronto

Description

DepartmentUnder general direction of the CTO, provides senior IT services and training in GPU programming, data science applications, and scientific computing workflows for the SciNet and SOSCIP advanced computing consortia which serve researchers at the University of Toronto and partner institutions, including industry researchers, faculty, postdoctoral fellows and graduate students in all disciplines and fields (e.g. science and engineering, medicine, finance, languages, etc.). The incumbent is involved in GPU-accelerated computing for data analytics and machine learning on large data sets (100TB and up). S/he works with researchers and research teams to plan, develop, install and optimize the SOSCIP GPU-Accelerated cluster for various research programs and provides technical consultation to researchers on their system needs for research operations. S/he also takes part in delivering and developing training and education on GPU applications.

SciNet and SOSCIP are sister organizations, co-located in the MaRS west tower. Launched in 2012, SOSCIP is a collaborative R&D consortium that brings together industry and academic researchers to undertake collaborative R&D projects using advanced computing. SOSCIP is led by the University of Toronto and Western University and its membership includes 13 other Ontario post-secondary institutions and IBM Canada.

SciNet operates large High Performance Computing (HPC) systems on SOSCIP’s behalf. SciNet provides HPC resources and support to researchers at the University of Toronto, the affiliated research hospitals, and other Canadian universities.

Duties include: assisting users (including remote users from across the country) with porting, writing, optimizing and running numerical codes on SOSCIP’s GPU platform; benchmarking user and commercial codes on existing and proposed computer systems; delivering optimized software products with appropriate documentation; maintaining smooth operations of hardware and software infrastructure including collaboration and visualization systems; establishing and supervising appropriate upgrades of the system; recommending equipment purchases and upgrades by analyzing user requirements and conducting economic and technical evaluations of different products; running workshops and courses on GPU programming, scientific computing, and data science for members of the university community; and acting as liaison with technical staff at other University-based HPC facilities. S/he may publish results from innovative algorithms and techniques.

Campus(MINIMUM)

Education:
M.Sc. in quantitative science or an acceptable equivalent combination of education and experience.

Experience:
Minimum five years’ experience with advanced computing scientific applications or large-scale, data- driven scientific computations. Significant experience and understanding of scientific numerical codes, compilers, code optimization, file input/output strategies, and data management.

Skills:
Excellent knowledge of computing hardware and networking. Solid knowledge of Fortran, C,C++ under Linux/Unix, and a scripting language like R and Python. Demonstrated ability to program efficiently on serial, vector and parallel systems, with the ability analyze and improve software codes and interface well with researchers across all disciplines on their computing needs.

Other:
Ability to learn on the job in a fast-paced environment. Ability to handle several tasks, and effectively prioritize and meet deadlines. Excellent verbal and written communication skills with the ability to communicate highly technical terms and concepts to people of non-IT background. Visualization expertise and presentation/training skills an asset.


Department: Office of the Vice President, Research & Innovation
Campus: St. George (downtown Toronto)
Schedule: Full-time
Job Field: Information Technology
Job Posting: Dec 2, 2019
Job Closing: Dec 13, 2019, 11:59:00 PM

  • c++
  • education
  • hardware
  • software