PhD: Physics-Informed Machine Learning for 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

PhD: Physics-Informed Machine Learning for Semiconductor Metrology

ARCNL is a new type of public-private partnership between the University of Amsterdam, the VU University Amsterdam, the NWO, and ASML.

Work Activities
How can we combine machine learning and physics to recover nanoscale information from imperfect images?
Modern computer chips are built with features only a few nanometers across, yet manufacturers need to measure these structures with extraordinary precision and do so quickly enough to keep up with large-scale production. This creates a fascinating computational challenge: how can we infer hidden physical properties from limited, noisy, and low-resolution measurement data?
In this project, you will develop a novel physics-informed machine learning approach that integrates physical simulations of the measurement process with its inverse reconstruction. A key challenge is the data-driven design of the experimental setup: exploring how the choice of measurements and configurations can be optimized to extract the most useful information for reliable parameter reconstruction.
You will work in close collaboration with the research department at ASML, the Centrum Wiskunde & Informatica (CWI, Prof. dr. Tristan van Leeuwen), and the AI4Science Lab, Informatics Institute, University of Amsterdam (dr. Patrick Forré), combining industrial relevance with academic depth in computational science and mathematical modeling.

Qualifications
You have (or soon will have) a MSc degree in computer science, machine learning, artificial intelligence, applied mathematics, physics, or a related discipline, meeting the Dutch university requirements for entry into a PhD program. 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 are motivated to work at the interface of academia and industry. Strong analytical skills, a collaborative mindset, and proficiency in verbal and written English are essential.

Work environment
ARCNL performs fundamental research, focusing on the physics and chemistry involved in current and future key technologies in nanolithography, primarily for the semiconductor industry. While the academic setting and research style are geared towards establishing scientific excellence, the topics in ARCNL’s research program are intimately connected with the interests of the industrial partner ASML. The institute is located at Amsterdam Science Park and currently employs about 100 persons of which 65 are ambitious (young) researchers from all over the globe. www.arcnl.nl

Working conditions
The position is intended as full-time (40 hours / week, 12 months / year) appointment in the service of the Netherlands Foundation of Scientific Research Institutes (NWO-I) for the duration of four years, with a starting salary of gross € 3.115 per month and a range of employment benefits . After successful completion of the PhD research a PhD degree will be granted at a Dutch University. Several courses are offered, specially developed for PhD-students. ARCNL assists any new foreign PhD-student with housing and visa applications and compensates their transport costs and furnishing expenses.

More information?
For further information about the position, please contact Lyuba Amitonova: [emailprotected] and Maximilian Lipp ([emailprotected] ).

Application
Online screening may be part of the selection.

Diversity code
ARCNL is highly committed to an inclusive and diverse work environment: we want to develop talent and creativity by bringing together people from different backgrounds and cultures. We recruit and select on the basis of competencies and talents. We strongly encourage anyone with the right qualifications to apply for the vacancy, regardless of age, gender, origin, sexual orientation or physical ability.

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Job details

Title

PhD: Physics-Informed Machine Learning for Semiconductor Metrology

ARCNL is a new type of public-private partnership between the University of Amsterdam, the VU University Amsterdam, the NWO, and ASML.