The Pittsburgh Supercomputing Center (PSC) is a joint effort of Carnegie Mellon University and the University of Pittsburgh. Established in 1986, PSC is supported by several federal agencies, the Commonwealth of Pennsylvania and private industry and is a leading partner in XSEDE (Extreme Science and Engineering Discovery Environment), the National Science Foundation cyberinfrastructure program.
PSC provides university, government and industrial researchers with access to several of the most powerful systems for high-performance computing, communications and data storage available to scientists, engineers and scholars nationwide for unclassified research. PSC advances the state of the art in high-performance computing, communications and data analytics and offers a flexible environment for solving the largest and most challenging problems in computational science.
The Scientific Applications and User Support group within PSC is responsible for helping to maximize the productivity of researchers. We are looking for a creative and capable Research Software Specialist to join an experienced team and contribute our part in pushing forward the boundaries of science.
In this role, you will be responsible for advanced support of computational scientists, engineers and scholars in a state-of-the-art converged high performance computing and data analytics environment. Critical duties include building and sustaining collaborative relationships with researchers to address complex challenges of developing, installing, porting, debugging and optimizing application-level research software systems. You will have the opportunity to publish results in peer-reviewed journals and at conferences, to develop documentation and training materials, and to engage in workforce development activities. You will thrive on collaboration with the PSC Artificial Intelligence & Big Data, Biomedical Applications, Allocations, Facilities Technology, and Network teams, as well as with partners outside PSC, such as colleagues in the XSEDE Extended Collaborative Support Service (ECSS).
Core responsibilities include:
Providing solutions to issues reported by PSC’s community of users, referred to you by the PSC helpdesk because of your expertise, in a timely manner
Collaborating with research teams in developing, installing, porting, debugging and optimizing application-level research software
Learning and keeping current with new technologies
Developing and maintaining user documentation of PSC application software
Bachelor's degree or equivalent, with strong computational emphasis required. Master's degree preferred
Minimum 3 years of relevant experience
Teaching experience preferred. Mentoring experience, especially in research preferred
Flexibility, excellence, and passion are vital qualities within the PSC. Inclusion, collaboration and cultural sensitivity are valued proficiencies at CMU. Therefore, we are in search of a team member who is able to effectively interact with a dynamic population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.
You should demonstrate:
Excellent interpersonal, oral and written communication skills with strong analytical ability
Knowledge of Linux/UNIX systems, shell scripting, and batch schedulers such as Slurm or PBS
Experience building, installing and maintaining application-level software
Proven debugging and troubleshooting skills
Experience with parallel computing architectures and software systems
High professional standards with the ability to manage competing requirements in a diverse user population
Expertise in one or more computational science disciplines, including bioinformatics and molecular dynamics
Experience with parallel programming models (e.g., use of OpenMP, MPI, CUDA) and with parallelization of serial computational applications
Experience with GPGPU computing, MATLAB, R, Python and with one or more mathematical/statistical programming packages: Python numpy/scipy/pandas, R, MATLAB, etc.
Familiarity with containerized execution such as Singularity, Docker, or Kubernetes