PhD: Physics-Informed ML for Nanoscale Metrology

ARCNL; Amsterdam; Hybrid; EUR 30,000 - 40,000; Full time

Benefits offered by this job

Housing assistance

Visa support

Transport cost compensation

Furnishing expenses

Job summary

ARCNL invites applications for a PhD position focusing on Physics-Informed Machine Learning for Semiconductor Metrology at Amsterdam Science Park. The project aims to recover nanoscale information from imperfect images by uniting physical simulations with data-driven reconstruction techniques.

Collaborators include ASML, CWI, and the AI4Science Lab at the University of Amsterdam. The role involves developing novel ML methods, exploring experimental design, and working closely with an

Qualifications

• You have a MSc degree in computer science, machine learning, artificial intelligence, applied mathematics, physics, or a related discipline.
• A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable.
• You are curious about combining physical modeling with data-driven methods and able to work at academia-industry interface.

Responsibilities

• Develop a physics-informed ML approach that integrates physical simulations with inverse reconstruction.
• Contribute to data-driven design of experimental setups to optimize information extraction.
• Collaborate with ASML, CWI, and AI4Science Lab to combine industrial relevance with academic depth.

Skills

Machine learning

Inverse problems

Scientific computing

English proficiency

Education

MSc in a relevant field

Job description

ARCNL invites applications for a PhD position focusing on Physics-Informed Machine Learning for Semiconductor Metrology at Amsterdam Science Park. The project aims to recover nanoscale information from imperfect images by uniting physical simulations with data-driven reconstruction techniques.

Collaborators include ASML, CWI, and the AI4Science Lab at the University of Amsterdam. The role involves developing novel ML methods, exploring experimental design, and working closely with an