Soft X-ray/EUV Plasma based-Optics & Microscopy

1. Background and Motivation Nonlinear optical processes and laser–plasma interactions offer opportunities for developing advanced microscopy techniques and novel light sources. Numerical simulations and computational image processing can help investigate these complex optical phenomena, understand image formation, and evaluate the potential of nonlinear optical microscopy for high-resolution imaging and optical metrology. Artificial intelligence (AI) offers additional opportunities to optimize imaging configurations, improve image reconstruction, and accelerate computational workflows. 2. Project Objectives This project aims to develop and investigate microscopy simulations and image-processing methods for nonlinear optical imaging involving laser–plasma interactions. The research will focus on understanding image formation, evaluating imaging performance, and exploring AI-based optimization strategies to improve system configurations and image reconstruction. 3. Methodology The project will begin with a literature review of nonlinear optics, laser–plasma interactions, and computational microscopy. The student will develop or adapt numerical models to simulate optical propagation, nonlinear interactions, and image formation under selected experimental conditions. Image-processing and reconstruction algorithms will then be implemented to analyse simulated and, where available, experimental data. AI-based methods, including machine learning or optimization algorithms, will be explored to optimize imaging parameters, improve reconstruction quality, and evaluate system performance. 4. Expected Outcomes and Learning Objectives The project aims to establish a simulation and image-processing workflow for investigating nonlinear optical microscopy and to demonstrate the potential of AI-assisted optimization. The student will gain experience in computational optics, numerical modelling, image processing, nonlinear optics, plasma-related optical phenomena, and AI-driven optimization. The results may support the future development of advanced microscopy systems and plasma-based optical technologies. Type of internship : Master internship Required educational background : Nanoscience & Nanotechnology, Physics, Computer Science, Mechanical Engineering Supervising scientist(s) : For further information or for application, please contact Hyun-su Kim ( Hyun-su.Kim@imec.be ) The reference code for this position is 2026-INT-100 . 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