Stochastic Acoustic Modeling Internship

ASML Germany GmbH; Eindhoven; On-site; EUR 13,000 - 20,000; Full time

Job summary

ASML's Mechanical Development and System Dynamics team in Veldhoven invites master's students to contribute to acoustic disturbance modeling for precision lithography systems. This internship focuses on extending a reduced-order framework by incorporating stochastic acoustic sources and validating with simulations and measurements.

You will perform literature reviews, develop modal descriptions for stochastic sources, implement representations within the model, and compare results with

Qualifications

• Pursuing a master's degree in applied physics, mathematics, mechanical engineering, or related field.
• Interest in computational acoustics and modeling.
• Basic knowledge of numerical methods and simulation techniques.
• Interest in dynamic measurements and signal processing.
• Basic experience with MATLAB.
• Good command of English for technical communication.

Responsibilities

• Conduct literature review on stochastic acoustic source modeling.
• Develop modal descriptions for stochastic sources.
• Investigate modeling approaches for point, line, and surface sources.
• Implement stochastic source representations within a reduced-order framework.
• Create and evaluate a modal stochastic source matrix.
• Compare simulation results with experimental measurements.
• Document findings and provide recommendations for future development.

Skills

Analytical

Communication

Education

Master's degree in applied physics

Related field (Math/ME)

Tools

MATLAB

Job description

ASML's Mechanical Development and System Dynamics team in Veldhoven invites master's students to contribute to acoustic disturbance modeling for precision lithography systems. This internship focuses on extending a reduced-order framework by incorporating stochastic acoustic sources and validating with simulations and measurements.

You will perform literature reviews, develop modal descriptions for stochastic sources, implement representations within the model, and compare results with