Participate in research on how to build trust in computational research in social sciences. This is a NSF-funded Research Experience for Undergraduates internship position. Applicant must be U.S. citizens, U.S. nationals, or U.S. permanent residents, be present in the US for the duration of the internship, and be a current undergraduate student at Cornell.
To apply: https://www.myworkday.com/cornell/d/inst/15$158872/9925$81757.htmld
Research, support, and build mechanism that allow for automated trusted computing in economics and more broadly social science research. You will participate in activities that focus on defining structured and actionable technical specifications; implement such specifications in continuous integration (CI) systems (on Bitbucket and Azure) and in HPC systems (SLURM clusters); and write papers describing such mechanisms. For more information on the project, see https://transparency-certified.github.io/
Develop and maintain Python scripts to integrate running arbitrary statistical analysis through trusted mechanisms.
Develop, test, and maintain configurations (Github and Bitbucket YAML) to automatically or on-demand trigger trusted computing.
Develop, test, and maintain SLURM configurations (for local SLURM cluster, for arbitrary third-party HPC systems) to use trusted computing mechanisms.
Adapt existing economics replication packages to fit within such systems.
Write papers that describe setup and outcomes of such systems.
Must have advanced Python experience
Must have understanding of statistical programming languages (R a requirement, Stata and Julia a plus)
Ability to write test suites in appropriate frameworks;
Good debugging and troubleshooting skills
Demonstrated ability to communicate with students/faculty as needed in a timely manner
Coursework and experience in major programming languages – preferred
Knowledge of HPC systems a plus
Knowledge of Docker a plus
Knowledge of SLURM a plus
Knowledge of Continuous Integration backends (Bitbucket Pipelines, Github Actions) a plus
Knowledge of Azure-based systems a plus
Scheduled Weekly Hours:
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Compensation:
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Federal Work Study Eligible:
EEO Statement:
Cornell University embraces diversity and seeks candidates who will contribute to a climate that supports students, faculty, and staff from all identities and backgrounds. We encourage individuals from underrepresented and/or marginalized identities to apply.
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The TRACE project is addressing challenges in trusting and verifying the results of computational research. The integrity of results is uncertain when their lineage or production cannot be validated, but verification through repeating executions is expensive and inefficient -- when possible. TRACE is developing a model of certified transparency whereby the original execution of a computational workflow is certified by the system on which they were run.