Student Intern Computational Geophysics

• Write accurate efficient and well-documented code.

• Integrate ML models with physics-based components and run controlled numerical experiments.

• Analyze model outputs and intermediate results; generate figures plots and diagnostic metrics to evaluate performance.

Disclaimer
The University of Kansas prohibits discrimination on the basis of race color ethnicity religion sex national origin age ancestry disability status as a veteran sexual orientation marital status parental status gender identity gender expression and genetic information in the universitys programs and activities. Retaliation is also prohibited by university policy. The following person has been designated to handle inquiries regarding the nondiscrimination policies and is the Title IX coordinator for all KU and KUMC campuses: Associate Vice Chancellor for Civil Rights and Title IX Room 1082 Dole Human Development Center 1000 Sunnyside Avenue Lawrence KSTTY.

Work Schedule
Onsite during business hours 8-5 M-F 20-40 hours per week negotiated during the interview.

Contact Information to Applicants
Annette Delaney

1. Currently pursuing a degree at the University of Kansas in Physics Mathematics Computer Science or a closely related quantitative discipline (e.g. Engineering Geophysics).
2. Strong programming skills in Python including experience with scientific computing libraries (e.g. NumPy SciPy Matplotlib) that can be demonstrated through previously authored software as evidenced in application materials.
3. Fundamental understanding of machine learning including supervised and/or unsupervised approaches as determined by course work publications or documentable experiences as evidenced in application materials.
4. Examples demonstrating ability to work with and analyze multi-dimensional data (e.g. time series images or volumetric datasets) as evidenced in application materials.

1. Experience with inverse problems and optimization theory through course work or documented experience including concepts such as gradient-based methods and objective function design as evidenced in application materials.
2. Familiarity with common DL architectures including Convolutional Neural Networks (CNNs) U-Net as evidenced with course work or professional experience as evidenced in application materials.
3. Experience working with GPU-accelerated computing environments as evidenced in application materials.
4. Experience with additional programming languages such as MATLAB and/or C particularly for scientific or high-performance computing applications with previous algorithms to demonstrate knowledge as evidenced by application materials.
5. Strong analytical and problem-solving skills as evidenced in course work or documentable experiences in application materials.

Position Overview
The Seismic Exploration group at Kansas Geological Survey is looking for motivated undergraduate students interested in pursuing a summer internship focused on Artificial Intelligence (AI) applications in geophysics. The selected candidate will contribute to the development of machine learning (ML) methodologies for seismic data analysis with an emphasis on near-surface characterization. The project involves designing and implementing deep learning (DL) models to process and interpret seismic datasets. Applicants should have a solid foundation in programming (e.g. Python) and a basic understanding of ML concepts including experience with supervised and/or unsupervised training approaches. 20-40 hours per week negotiated during the interview.


Additional Candidate Instruction
Complete the student profile in the KU Brassring Recruitment system and upload the following:

1. Resume
2. Cover letter addressing how you meet the qualifications for the position is welcome but not required
3. Contact information for at least two professional or academic references (include relation to each reference)

To ensure consideration apply before the application review starts on Tuesday 4/28/2026. A review of applications will continue and is ongoing until a pool of qualified candidates is identified.



Employment Conditions
(1) Student employees are not eligible for remote work arrangements unless an exception is approved by the unit leadership and HR.

(2) This is an hourly/non-exempt position. A student employee cannot hold an hourly/non-exempt position and a salaried/exempt graduate or undergraduate position (GRA/GTA/GA/RUA/RGA) concurrently.

(3) Work for this position will be performed both independently and collaboratively.