onsite
Machine Learning Scientist - Physics Informed - ASML
ML Engineer
Develop and apply physics‑informed machine learning models to improve lithography system performance, leveraging Python, deep‑learning frameworks, and HPC resources.
About the role
Key Responsibilities
- Design, implement, and train physics‑informed neural networks to model and optimize lithography processes.
- Collaborate with optical engineers and domain scientists to integrate physical constraints into data‑driven models.
- Develop scalable training pipelines using CUDA‑accelerated GPUs and high‑performance computing clusters.
- Analyze large‑scale experimental and simulation datasets to extract insights and validate model predictions.
- Publish research findings internally and contribute to patents that advance semiconductor manufacturing technology.
Requirements
- Ph.D. in Computer Science, Applied Mathematics, Physics, or a related field with a focus on machine learning or scientific computing.
- Strong proficiency in Python and deep‑learning libraries such as PyTorch or TensorFlow.
- Hands‑on experience with physics‑informed neural networks, numerical methods, and statistical modeling.
- Proven ability to develop and optimize GPU‑accelerated code using CUDA or similar technologies.
- Excellent problem‑solving skills and the ability to work cross‑functionally with hardware and domain experts.
Skills
pythonpytorchtensorflowcuda