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

Imec; Vlaams-Brabant; Presencial; EUR 6700 - 10.000; Jornada completa

Descripción de la vacante

imec invites applications for a Master internship in Soft X-ray/EUV Plasma based-Optics & Microscopy, based in Brussels. The project focuses on developing and evaluating nonlinear optical microscopy through simulations and image-processing under laser-plasma interactions, with AI-based optimization of imaging configurations and reconstruction workflows.

You will gain hands-on experience in computational optics, numerical modeling, and AI-driven optimization, contributing to future plasma-based

Formación

• Background in nanoscale science and nanotechnology.
• Strong grounding in physics principles relevant to optics.
• Experience or coursework in computational methods or image processing is beneficial.

Responsabilidades

• Develop or adapt numerical models to simulate optical propagation and nonlinear interactions.
• Implement image-processing and reconstruction algorithms for simulated and experimental data.
• Explore AI-based methods to optimize imaging parameters and reconstruction quality.
• Report on findings and contribute to the advancement of microscopy techniques.

Educación

Nanoscience & Nanotechnology

Physics

Computer Science

Mechanical Engineering

Descripción del empleo

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

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

Master internship - Brussel | About a week ago

Explore and advance state-of-the-art optical technologies for semiconductor and biomedical applications

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 ( [e-mail] )

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

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 ( [e-mail] )

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.

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