Physics | Materials Science internship ai-driven experimental insights
Eindhoven - Netherlands
Job Summary
Within the Materials and Chemistry Group at ASML Research in Veldhoven you will contribute to advancing knowledge from complex experimental data. Our team uses a wide range of experimental and analytical methods to understand physical phenomena that support future technology development. A key challenge is connecting results from different test environments and understanding how they relate to real-world operating this internship you will explore how Artificial Intelligence and Machine Learning can help bridge these gaps and support data-driven innovation. This Physics Materials Science internship offers the opportunity to work on meaningful research challenges in a multidisciplinary environment.
Your assignment
As part of this internship you will investigate how Artificial Intelligence and Machine Learning methods can improve the interpretation of experimental and analytical datasets. You will explore relationships within complex data and evaluate how observations can be translated between different environments and real-world conditions. Working closely with researchers and domain experts you will combine data-driven approaches with physical understanding to generate valuable insights. Your main responsibilities will be:
Explore experimental datasets and understand challenges related to comparing different environments
Evaluate Artificial Intelligence and Machine Learning methods for heterogeneous data analysis
Develop and assess data-driven models that connect observations across experimental conditions
Analyze results and identify meaningful patterns and relationships within complex datasets
Collaborate with researchers from different technical disciplines to interpret findings
Assess the potential impact of AI-driven knowledge extraction for technology development
Present conclusions and recommendations through reports and presentations
This is a masters (thesis) internship for minimum 5 months minimum 4 days per week (hybrid). The starting date of this internship is projected for October 2026.
Your profile
To be suitable for the internship you:
Are pursuing a masters degree in Physics Materials Science Applied Physics Engineering Data Science or a related technical field
Have a strong interest in Artificial Intelligence Machine Learning and data-driven modeling
Have experience with data analysis and programming preferably using Python or similar programming languages
Are analytical proactive and comfortable working independently while collaborating with others
Have strong English communication skills both written and spoken
Other requirements you need to meet
You are enrolled at an educational institutefor the entire duration of the internship;
You need tobe located inthe Netherlands to beable toperform your case you re currently living/studying outside of the Netherlands your CV/motivation letter includes the willingness torelocate.
If you are a non-EU citizen studying in the Netherlands your university is willing to sign the documents relevant for doing an internship (i.e.Nufficagreement).
This position requires access to controlled technology as defined in the United States Export Administration Regulations (15 C.F.R. 730 et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.
ASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit hire train and promote persons in all job titles without regard to race color religion sex age national origin veteran status disability sexual orientation or gender identity. We recognize that inclusion and diversity is a driving force in the success of our company.
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Required Experience:
Intern
About Company
ASML gives the world's leading chipmakers the power to mass produce patterns on silicon, helping to make computer chips smaller, faster and greener.