High Speed Spectrally Coded Illumination- and Machine Learning- based Hyperspectral Microscope imagi

This project presents a high-speed hyperspectral microscopy framework that integrates spectrallycoded illumination with advanced machine learning algorithms to achieve rapidand accurate spectral imaging at microscopic scales. By projecting optimizedspectral codes through a programmable illumination module, the system enablesfast acquisition of spectrally enriched measurements without relying on slowwavelength scanning mechanisms. A learning-based reconstruction pipeline then transforms these encodedmeasurements into fast spectralcontrast images, preserving fine spectral signatures essential for materialidentification. Leveraging these reconstructed spectra, machine learningmodels accurately classify materials used in semiconductor devices, even whentheir optical characteristics differ only subtly. This approach provides acompact, efficient, and high throughput solution for next generationnanomaterial inspection, semiconductor characterization, and computationalmicroscopy applications.

Responsibilities:

• Computational and Deep learning-based imaging (Matlab, Python)
• Optics performance measurements
• Assist of system Configuration of hyperspectral microscope

Type of internship: Master internship, PhD internship

Required educational background: Bioscience Engineering, Computer Science, Materials Engineering, Mechanical Engineering, Nanoscience & Nanotechnology, Physics

Supervising scientist(s): For further information or for application, please contact Hyun-su Kim ([email protected])

The reference code for this position is 2026-INT-050. Mention this reference code in your application.

Only for self-supporting students.

Applications should include the following information:

• resume
• motivation
• current study

Incomplete applications will not be considered.