University of Texas at Austin
Moncrief Interns

Program Contact

For questions regarding this program, please contact:

Interested in learning more about our interns' Moncrief experience? Check out these articles:

Crossing Disciplines: Insights from the Moncrief Interns of 2024

Unlocking Potential: Insights from the Moncrief Interns of 2023

Moncrief Summer Internship

Deadline: February 1, 2025

The Moncrief Undergraduate Summer Internship Program provides summer support for qualified undergraduate students of mathematics, science, and engineering to work within the Oden Institute for Computational Engineering and Sciences during the summer months.

Interns work with faculty and research staff from one of the Oden Institute's research centers and groups on one of a wide range of research topics. Research activities generally focus on developing modeling and simulation methods to study problems in areas such as energy, environment, advanced materials, biomedical research, nanomanufacturing, autonomous systems and many other scientific and engineering areas that draw on applied and computational mathematics, computing, fluid mechanics, solid mechanics, mathematical physics, and biology.

Eligibility Requirements

For participation in the Summer 2025 program applicants must be U.S. citizens or permanent residents* who are currently enrolled in an undergraduate institution and will be entering their junior or senior year in Fall 2025.

*International students may apply if they are currently enrolled at the University of Texas at Austin.

Internship Information

  • Dates: June 2 – August 8, 2025
  • Support: the internship will provide a $6,000 stipend paid directly to the student, plus housing in a UT Austin dorm, a meal plan, and some funding to defray travel costs to/from Austin. UT Austin students with existing housing may elect for a housing stipend in lieu of on campus housing and dining
  • Project assignment: Interns will be assigned a faculty supervisor in one of the Oden Institute's Research Centers or Groups and will work with faculty, graduate students, and postdocs.

Application Deadline: February 1, 2025

Applicants will be notified of decisions by March 7, 2025.

How to Apply

  1. Submit Internship Online Application Form

    Submit the following materials to the Internship Coordinator via the Application Materials Uploader located below:

  2. Resume
  3. Personal Statement — write a 1-page (double-spaced) statement describing why you wish to participate in the Moncrief Summer Internship Program. You may include your past & current research experience and state your post-graduation career goals.
  4. Official transcript(s).
    1. Non UT Austin students: PLEASE NOTE — this is a COPY of your official transcript (i.e. get your official transcript, scan it, and then upload it). Copies of unofficial transcripts (such as those printed from your university's website) will not be accepted.
    2. UT AUSTIN STUDENTS: do not need to upload their UT transcript. Provide your UT EID on the application form and we will access your transcript.
  5. Two (2) letters of recommendation (letters should uploaded directly by your references. Please provide them with the link to the Application Materials Uploader. PDF format is preferred.)

Please label all files in the following format: Last Name, First Name_File Type (i.e., Application, Resume, Personal Statement, Letter of Recommendation, Transcript)

Application Materials Uploader

Previous summer research topics

Comparing Activation Functions to Corresponding B-functions

Supervisor: Tan Bui-Thanh

Machine Learning for Molecular Dynamics

Supervisor: Don Siegel

Autonomy Simulations in STK and Onesky

Supervisor: Ufuk Topcu

Biomarker Identification of Low-Quality Cells for Single Nuclei RNA-Seq

Supervisor: Stephen Yi

Analysis of Model Prediction for Behavior Based on Neural Activities with t-SNE Visualization of Network Embeddings

Supervisor: Atlas Wang

Efficient High-Speed Neural Network Models for Biological Materials

Supervisor: Michael S. Sacks

Determination of fall-risk in transitional movements using markerless motion capture techniques

Supervisor: Joshua Chang

Implementing and Benchmarking Learning Algorithms for Neural Network Potentials

Supervisor: Graeme Henkelman

A framework for stochastic, size-structured neutral model for community

Supervisor: Annette Ostling

NODE-FNO and C-FNO: Improve Fourier Neural Operators through Adjoint Training and Conservation Enforcing

Supervisor: Chandrajit Bajaj

Employing magnetic resonance imaging data to implement a mathematical model of glioblastoma progression

Supervisor: Tom Yankeelov

Modeling Single File Dynamics using the Generalized Langevin Equation

Supervisor: Dmitrii E. Makarov