Recruiting postdocs, research staff, students, and visitors
Push multimodal representation learning toward problems that matter: geospatial AI, biodiversity and conservation, generative modeling, and medical imaging.
Open Positions
Taylor Geospatial funded · In person, St. Louis · ~2 years, starting Summer 2026 or later (extensions possible)
Join a multi-institution project on next-generation neural-field representations of Earth observation data—continuous, queryable representations of the planet that can be updated as new observations arrive and that report calibrated uncertainty over user-specified regions and time ranges. Funded by Taylor Geospatial, with engagement on the broader Features of the World initiative. Strong publication record at top-tier computer-vision and machine-learning venues (CVPR, NeurIPS, ICLR, ICCV, ECCV) required.
Why Join MVRL
The Multimodal Vision Research Lab fosters a culture of technical excellence and collaborative discovery. Our members publish regularly at top-tier venues (CVPR, ICCV, ECCV, NeurIPS) and work on interdisciplinary projects with real-world impact. Our research centers on multimodal representation learning, geospatial AI, biodiversity and conservation, and generative modeling over earth observation data—with additional applications in medical imaging where our methods address scarce and heterogeneous clinical data.
- High-Impact Venues
- Global-Scale Data
- Interdisciplinary Focus
- Modern Infrastructure
- Collaborative Spirit
- Career Mentorship
Frequently Asked Questions
I'm a high school student interested in research with the lab. Can I get involved?
We do not currently take on high school students. The best preparation is to build a strong foundation in machine learning and computer vision—work through introductory courses, get comfortable with PyTorch, and read recent lab papers on topics that interest you. Once you're an undergraduate with some experience, please feel free to reach back out.
I'm a current WashU undergraduate or MS student. How do I get involved?
You're welcome to submit the interest form above, and we encourage it. In parallel, we typically defer to our PhD students to choose who they collaborate with on day-to-day projects—so it can also help to look through our research areas and recent papers, identify a PhD student whose work overlaps with your interests, and reach out to them directly to ask whether there's a way you can contribute.
Are there formal summer programs I can apply to?
Yes—several WashU-administered programs place undergraduates with research labs (including ours) for the summer. Applications typically open in December and close in mid-February; check each program page for exact dates.
For these programs, you must apply directly through the program itself—just mention your interest in working with MVRL in your application.
I want to apply to MVRL for a PhD. What should I do?
The interest form above does not replace a formal PhD application. Prospective students apply through the WashU PhD program in Computer Science, Imaging Science, or Electrical & Systems Engineering; deadlines are typically in mid-December. We recommend attending the appropriate recruiting Info Session in the fall, and you are welcome to also submit the interest form so we know you are applying.
I'm interested in a postdoc or visiting researcher role.
Please submit the interest form above. The form has fields for your funding situation (including external fellowships, home-institution support, or self-funded visits), so use those to tell us what kind of arrangement you have in mind.
What kind of background are you looking for?
The specifics depend on the role, but in general we look for:
- Strong programming skills, primarily in Python.
- Solid foundations in machine learning and computer vision, with hands-on experience using PyTorch or a similar framework.
- Mathematical maturity in linear algebra, probability, and optimization.
- Clear written and oral communication—research only matters if it can be shared.
- A collaborative spirit. Most of our work involves close collaboration across students, postdocs, and external partners.
- For PhD and postdoc applicants, a track record (or strong potential) for publishing at top venues such as CVPR, ICCV, ECCV, or NeurIPS.