Scientist Track
Home / Scientist TrackAbout the Scientist Track
The Scientist Track supports research in various space science and technology fields
using state-of-the-art AI technologies.
First, we are receiving creative and challenging research proposals that match the
objectives of this program in order to identify important research topics.
After the submission deadline, the SpaceAI Scientific Organizing Committee will
review all proposals in three major aspects
(scientific relevance of the topic, feasibility of constructing appropriate datasets
for AI model development, and the importance and impact of the expected outcomes),
and then select the research projects to be carried out in this track this year.
For each selected project, we will recruit experts who wish to join as team members,
organize teams, provide tailored consulting, offer platforms for big data and AI model
development, and support efficient collaboration through workshops, intensive collaboration
camps, and conferences.
Track Participation Application
We are accepting applications from experts who wish to join the selected research projects as team members and play a leading role in collaborative research and development. By forming teams with the proposal submitters and participating in workshops, online team meetings, and intensive collaboration camps, participants will be supported to publish papers in international journals, present at domestic and international conferences, file patents for resulting technologies, and develop APIs for various services. We welcome experts from diverse fields, including space science, space technology development, big data science, machine learning, image processing, statistical analysis, and hardware (GPU). For inquiries regarding participation, please contact us by email (spaceai_loc@kasi.re.kr).
- Application period: April 13 (Mon) – May 15 (Fri), 2026
- Contact: spaceai_soc@kasi.re.kr
Research Proposal Submission
We are accepting research proposals that describe diverse topics and methods for applying artificial intelligence in the fields of astronomy, space science, and space technology. The submission deadline is May 15. After the deadline, the Scientific Organizing Committee will review all proposals and individually notify applicants of the evaluation results and selection status. If you have any questions regarding proposal preparation or review, please contact us by email (spaceai_soc@kasi.re.kr).
- Application period: April 13 (Mon) – May 15 (Fri), 2026
- Contact: spaceai_soc@kasi.re.kr
* Submitting a research proposal is optional.
SpaceAI 2026 Conference Participation
- - Time: February 3, 2026 (Tue), 1:30 PM – 5:00 PM
- - Venue: Milky Way Hall Small Auditorium, Korea Astronomy and Space Science Institute
- - Note: No registration fee
- Registration period: January 16 (Fri) – February 2 (Mon), 2026
- Contact: spaceai_soc@kasi.re.kr
Conference Program
List of Research Projects
Space Science
2023
- • Development of a Time-Series Prediction Model for Solar High-Energy (>10 MeV) Proton Flux Observed at Geostationary Orbit (K. Yi et al., KHU)
- • Development of a Prediction Model for Magnetic Field Evolution in Solar Active Regions (H. Lee et al., KHU)
- • Prediction of the Next Solar Rotation Synoptic Maps Using an AI–based Surface Flux Transport Model (H.-J. Jeong, KHU)
- • Detection and Cataloging of Magnetic Clouds from Solar Wind Observations (R. Kim et al., KASI)
- • Modeling of Extreme Ultraviolet Irradiance Based on Soft X-ray Flux During Solar Flares (S.-H. Park et al., KASI)
- • Development of a Deep Learning-Based Prediction Model for Soft X-ray and Extreme Ultraviolet Fluxes During Solar Flares (J.-H. Kim et al., KASI)
- • Automatic Detection of Geomagnetic Waves Observed by Ground-Based Magnetometers Using Deep Learning-Based Image Clustering (J. Kwak. et al., KASI)
- • Development of a Deep Learning-Based Method for Deriving Three-Dimensional SHARP Parameters from Line-of-Sight Solar Magnetograms (J. Son, KHU)
2024
- • Development of a Model for Aurora Identification and Auroral Oval Boundary Prediction Using Satellite Images (S. Jeon et al., CNU)
- • Development of a Deep Learning-Based Method for Aurora Detection Using All-Sky Auroral Images (Y. Cho et al., KOPRI)
- • Prediction of Heavy Ion Charge State Ratios in the Solar Wind Using In Situ Satellite Observations (J. Seough et al., KASI)
2025
- • Development of a Deep Learning Model for Predicting Solar Energetic Particle (SEP) Events (S. Lee et al., KASI)
- • Development of a Deep Learning-Based Forecasting Model for the Geomagnetic Disturbance Index (Kp) at Mid-Latitudes (J. Son et al., KASI)
Astronomy and Astrophysics
2024
- • Prediction of the Extragalactic Radio Sky from Optical Galaxy Surveys Using Artificial Intelligence (M. Yang et al., KASI)
2025
- • Detection and Temporal Evolution of Galaxy Collision and Merger Events Using Deep Learning-Based Multi-Modal Galaxy Data (W.-B. Zee et al., Sejong University)
Space Technology
2023
- • Optimization of Mirror Polishing Processes for Space Astronomy Observations Through Input–Output Correlation Analysis (J.-Y. Han et al., KASI)
- • Machine Learning-Based Calibration of Magnetometers Using Satellite Data and Geomagnetic Field Models (H. Song, KASI)
- • Image Analysis Using AI Models Deployed on a CubeSat On-Board Computer (B. Cho, I-Trix)
2024
- • Development of an AI-Based Optical System Alignment Method Using Wavefront Error Information (Y. Kim et al., KASI)
- • Research on Satellite Onboard AI-Based Technologies (S. Hwang et al., KHU)
- • Selection of Extreme Ultraviolet Wavelengths for the L4 Mission (D. Kim, KHU)
- • Implementation of an Auto-Guiding System Using the Center of a Target Positioned on the Slit (H. Lee, UJU Softlab)
- • Development of a Deep Learning Model for Segmented Mirror Alignment (S. Han, KASI)