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Department Overview:
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The Open Source Program Office (OSPO) is looking to connect interns with meaningful open-source projects as part of a new cohort of the internship program in collaboration with Madison College. During the internship, students will join a mentored open source project, participate in an initial training session, and weekly check-ins with the Open Source Program Office, and learn crucial skills related to managing open source software projects and growing software user communities. |
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Anticipated Start Date:
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2/2/2026 |
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Anticipated End Date (If Applicable):
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5/1/2026 |
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Remote Work Eligibility Detail:
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Partially Remote
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Anticipated Hours Per Week:
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Minimum: 10 Maximum: 15 |
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Schedule:
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Internship work schedules will be established in collaboration with the project mentors, with a general expected commitment of 10-15 hours/week. In addition to the work schedule established with the project lead, interns will participate in a weekly group session with the OSPO for check-ins, trainings, and guest speakers. |
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Salary/Wage Range/Lump Sum:
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Minimum: $15.00 Maximum: $17.00 |
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Number of Positions:
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1 |
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Qualifications:
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UW-Madison and Madison College undergraduate and graduate students with applicable backgrounds in any field are eligible to apply. Students must be enrolled in a degree program during the calendar year with at least one semester remaining after the internship’s conclusion.
Application materials should include:
- A one-page cover letter that highlights your qualifications based on skills identified in the project listing and your interest in open source broadly.
- A resume that includes your name, school email address, phone number, field(s) of study (major, minor, degree, certificate), relevant coursework, extracurricular activities, expected graduation date, relevant sample work (ex: GitHub link, personal website, etc.) and any relevant work or research experience.
-The names and contact information of three references.
Submit a resume, cover letter, and three references as part of your application. |
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Knowledge, Skills & Abilities:
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The intern must be fluent in Python programming and familiar with machine learning tools and frameworks such as scikit-learn, PyTorch, or TensorFlow, as all development will be conducted in Python. Essential skills include experience with data analysis and visualization libraries (numpy, pandas, matplotlib), understanding of ML concepts including training/validation/testing, overfitting, regularization, and cross-validation, and comfort with Git/GitHub for version control. Strong problem-solving abilities, independent work skills, and excellent written and verbal communication are required, along with genuine interest in astronomy and willingness to learn about stellar physics.
The ideal candidate is a sophomore or junior undergraduate student (preferred to enable multi-semester engagement) who is interested in continuing work over multiple semesters and pursuing first-author publication. No prior astronomy research experience is required.
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Position Summary/Job Duties:
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This astronomy research project focuses on developing a machine learning tool to obtain reliable stellar ages from rotation periods. The project will create an open-source Python package called the "Machine Learning Gyrochronology Age Estimator" that automatically estimates stellar ages using gyrochronology—the method of dating stars by measuring how fast they rotate.
Scientific Goal: Stars slow their rotation as they age due to magnetic braking. By measuring a star's rotation period and comparing it to calibration samples (star clusters with known ages), we can estimate stellar ages. This is critical for understanding planetary system evolution, stellar physics, and galactic history. Unlike simple interpolation methods, this tool will use sophisticated machine learning algorithms to capture complex relationships between rotation periods, stellar properties (color, mass, metallicity), and age while providing reliable uncertainty estimates.
Technical Goal: Create an end-to-end automated pipeline that takes only a Gaia DR3 source ID (stellar identifier) as input and automatically: (1) queries stellar parameters and time-series photometry, (2) computes rotation periods from light curves, and (3) estimates ages with error bars using ML models trained on benchmark clusters spanning 50 million to 3+ billion years old.
Current Status: Concept phase. This project would be developed from scratch using Python, leveraging existing astronomy packages (astroquery, lightkurve, astropy) and ML frameworks (scikit-learn, PyTorch, TensorFlow). Training datasets will be provided by the research team.
Open Source Commitment: The complete project will be made publicly available on GitHub with comprehensive documentation, tutorials, and contribution guidelines. This will benefit the broader astronomy community and provide the student intern with a prominent portfolio piece and first-author publication opportunity.
The intern will contribute across the full development pipeline of this astronomy software project. Core responsibilities include implementing automated data retrieval systems in Python to query Gaia DR3, TESS, ZTF, and other astronomical archives with cross-matching algorithms; developing time-series analysis routines to detect stellar rotation periods from light curves using algorithms like Lomb-Scargle periodograms and Gaussian processes; building and optimizing machine learning models trained on provided cluster datasets to predict stellar ages with uncertainty quantification; and creating comprehensive documentation, tutorials, and a user-friendly command-line interface. The intern will also contribute to scientific analysis, figure preparation, and manuscript writing as first author (or second author if preferred) on a peer-reviewed journal article.
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Physical Demands:
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Interns are expected to be able to sit for extended periods. Specific physical demands will be discussed with mentors during the interview process.
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Institutional Statements:
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Equal Employment Opportunity Statement:
UW-Madison is an Equal Employment, Equal Access Employer committed to increasing the diversity of our workforce.
Institutional Statement on Diversity:
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals.
The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background-people who as students, faculty, and staff serve Wisconsin and the world.
For more information on diversity and inclusion on campus, please visit: diversity.wisc.edu
Accommodation Statement:
If you need to request an accommodation because of a disability, you can find information about how to make a request at the following website:https://employeedisabilities.wisc.edu/disability-accommodation-information-for-applicants/
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