About Us
The Computational Cognitive Neuroscience Lab investigates the computational principles by which the human brain supports intelligence, learning, and flexible decision-making. We’re interested in how the brain makes sense of different experiences and turns them into useful knowledge. We study how it builds internal models of the world—mental structures that help us reason, predict what might happen next, and make smart decisions in new situations.
Our research combines behavioral experiments, neuroimaging techniques (fMRI, OPM-MEG, and EEG), and computational modeling, including both normative models and neural networks. By integrating approaches from psychology, neuroscience, machine learning, and behavioral economics, we aim to understand how the brain represents concepts, learns structural relationships, and makes novel inferences.
Join Us
Postdoctoral or Graduate student positions
For information about current openings (postdoctoral or graduate student positions), please click here: Open positions
Undergraduate Research Opportunities
CCN Lab is seeking motivated undergraduate students from any department who are interested in gaining hands-on research experience. Our lab investigates how the brain supports learning, decision-making, and flexible behavior using tools from cognitive neuroscience, neuroimaging, and computational modeling. We welcome students from a wide range of academic backgrounds. If you’re excited to contribute to a collaborative and interdisciplinary research environment, we encourage you to reach out!
As an undergraduate researcher, you’ll have the opportunity to:
- Work with lab members to collect behavioral and neuroimaging data
- Learn how to analyze the data you helped collect
- Join lab meetings and journal clubs to discuss state-of-the-art research in cognitive neuroscience
Commitment & Expectations:
This is an unpaid position, but we ask for a commitment of at least 3 hours per week (or 9 hours per week if you would like to receive research credit, such as PSYC 2994 or PSYC 4994). We expect students to take ownership of their roles and contribute responsibly to ongoing projects. This opportunity is especially valuable for students considering graduate programs in cognitive neuroscience or related fields. Prior coursework in neuroscience and/or machine learning is helpful but not required. Most of our work is experimental and data-driven. If you are only interested in purely theoretical research, this may not be the best fit. However, we value curiosity, collaboration, and a strong desire to learn.
Please note that while we are excited to support student involvement, our capacity to provide meaningful research experiences is limited by available resources. If we are unable to offer you an active role immediately, you are still welcome to join our lab meetings and journal clubs, where we discuss cutting-edge findings in neuroscience and cognitive science.
Application Process:
Before joining the lab as a research assistant (RA), we recommend that you first become familiar with our research through the following activities:
- Participate in our experiments as a research participant. This will help you understand the types of studies conducted in our lab and the experience of being a participant.
- Join our lab Google Group: https://groups.google.com/u/3/a/vt.edu/g/ccnf-g
To join the Google Group, please use your VT account (you may need to add your VT account to Google first). - Attend our lab meetings and journal clubs. The schedule can be found after you join the Google Group.
If you are interested in working with us after participating in our experiments and attending lab meetings, please read the “RA Information and Expectations” document in the Google Group thread before applying.
To join as an RA in the following semester, please contact me during the summer or winter break rather than in the middle of the semester. This allows sufficient time to complete required training, identify appropriate projects, and arrange your role in the lab. To apply, please email your CV to spk@vt.edu and include:
- Your major and year
- Research experience
- Related coursework (e.g., Psychology, Neuroscience, Computer Science, Machine Learning, Statistics, Mathematics, Engineering, and/or Physics)
- Programming skills (e.g., Python, MATLAB, R, Julia, etc.)