Casual Nonexempt - 0005

Required Qualifications:

• Undergraduate student at Macalester College
• Proactive & Independent: Ability to work independently and take initiative.
• Organizational Excellence: Proven ability to manage multiple tasks, prioritize effectively, and maintain meticulous attention to detail.

• Communication: Excellent written and verbal communication skills.

Preferred Qualifications:

• Experience with computational topology software (e.g., GUDHI, Ripser, giotto-tda, or similar).
• Familiarity with natural language processing (NLP), network science, or embedding methods.
• Experience with machine learning libraries such as PyTorch, TensorFlow, or scikit-learn.
• Interest in or exposure to the philosophy or sociology of science, scientometrics, or innovation studies.
• The ability to work both independently and collaboratively.

Expected Start Date: May 26, 2026

Expected End Date: August 30, 2026

Supervisor: Professor Russel Funk

Job Responsibilities:

Data Wrangling:

• Process and manage large-scale bibliometric datasets
• Format and structure datasets for quantitative data analysis, ensuring integrity, consistency, and relevance.

Quantitative Data Analysis:

• Conduct in-depth quantitative data analysis using techniques such as regression analysis, topological data analysis, network analysis, machine learning, and natural language processing.
• Analyze text and other metadata from millions of research articles and patents, drawing meaningful insights and conclusions.

• Assist in the preparation of research manuscripts, ensuring relevance, accuracy, and coherence. This will include the potential for authorship.
• Prepare slide decks and present research findings at conferences and meetings.
• Develop and prepare software code relevant to the project for public release, ensuring functionality, user-friendliness, and proper documentation.

Work Location: Minnesota Carlson

Hours per Week: This is a casual position, with hours less than 14 per week. Time Card submissions are required for this role.
Work Days/Times: Determined by employee and Supervisor

Applications must be submitted online. To be considered for this position, please click the Apply button and follow the instructions. You will have the opportunity to complete an online application for the position and attach a resume.

Additional documents may be attached after application by accessing your "My Job Applications" page and uploading documents in the "My Cover Letters and Attachments" section.

To request an accommodation during the application process, please e-mail employ@umn.edu or call (612) 624-8647.

The University recognizes and values the importance of diversity and inclusion in enriching the employment experience of its employees and in supporting the academic mission. The University is committed to attracting and retaining employees with varying identities and backgrounds.

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