Physics‑Informed ML for Nanoscale Semiconductor Metrology

Karlstad University; Amsterdam; On-site; EUR 35,000 - 37,000; Full time

Benefits offered by this job

Housing assistance

Visa assistance

Transport cost coverage

Job summary

ARCNL, a public-private partnership in Amsterdam, invites applications for a PhD: Physics-Informed Machine Learning for Semiconductor Metrology. The PhD will develop physics-informed ML methods that couple physical simulations with inverse reconstruction of nanoscale semiconductor measurements.

The successful candidate will join a collaborative network with ASML and the University of Amsterdam, addressing challenging computational science and mathematical modeling at the interface of industry

Qualifications

• MSc degree in computer science, machine learning, artificial intelligence, applied mathematics, physics, or related discipline.
• Background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable.
• Curiosity about combining physical modeling with data-driven methods and working at academia-industry interface.
• Strong analytical skills, collaborative mindset, and proficiency in verbal and written English.

Responsibilities

• Develop a novel physics-informed machine learning approach that integrates physical simulations of the measurement process with its inverse reconstruction.
• Design data-driven experiments to optimize measurement configurations for reliable parameter reconstruction.
• Collaborate with ASML, CWI, and AI4Science Lab to combine industrial relevance with academic depth.

Skills

Analytical skills

Collaborative mindset

Verbal English

Written English

Education

MSc degree in Computer Science, ML, AI, applied mathematics, physics, or related discipline

Job description

ARCNL, a public-private partnership in Amsterdam, invites applications for a PhD: Physics-Informed Machine Learning for Semiconductor Metrology. The PhD will develop physics-informed ML methods that couple physical simulations with inverse reconstruction of nanoscale semiconductor measurements.

The successful candidate will join a collaborative network with ASML and the University of Amsterdam, addressing challenging computational science and mathematical modeling at the interface of industry