Job Description

Position:
Student Employee – Workload Classification Framework Enhancement

We are seeking a motivated and detail-oriented student employee to support the refinement and expansion of a rule-based workload classification framework within our HPC environment. This role will contribute to the development of real-time job tagging, metadata extraction, and advanced classification techniques, and help integrate visualizations into comprehensive dashboard systems.

Key Responsibilities:
Enhance rule-based logic for real-time job tagging based on job metadata and runtime characteristics.
Develop and implement methods for automated extraction of storage locations from job metadata.
Introduce and evaluate supervised and unsupervised classification models to improve workload categorization.
Collaborate with team members to integrate classification visualizations into existing dashboard platforms.
Document processes, findings, and improvements for internal knowledge sharing and future development.

Required Qualifications:
Currently enrolled in a Bachelor's or Master's program in Computer Science, Data Science, Engineering, or a related field.
Familiarity with Python and data processing libraries (e.g., Pandas, NumPy).
Understanding of basic machine learning concepts and classification techniques.
Experience working with structured and unstructured data.
Strong analytical and problem-solving skills.
Ability to work independently and communicate effectively in a team setting.

Preferred Skills and Experience:
Experience with job schedulers (e.g., LSF, Slurm, PBS) and HPC environments.
Knowledge of metadata parsing and log analysis.
Exposure to data visualization tools (e.g., Plotly, Dash, Grafana).
Familiarity with dashboard integration and web-based UI frameworks.
Prior experience with rule-based systems or workflow automation.

Benefits of the Role:
Hands-on experience in a high-performance computing environment.
Opportunity to contribute to impactful infrastructure and analytics tools.
Mentorship from experienced professionals in HPC and data science.
Flexible schedule to accommodate academic commitments.

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