Mathematics Machine Learning Engineer

Do you enjoy solving algorithm design problems for the semiconductor metrology industry, with demanding time, accuracy, and memory requirements? Do you like to use your creativity, your in-depth knowledge of physics principles, numerical math's and machine learning, and your hands-on experience with practical problem solving, being part of a highly talented group of algorithm experts?

Role and responsibilities

In this role you will focus on scanner modeling and optical and mathematical models and methods required to infer physical model parameters from optical metrology data. Relevant new metrics, calibrations and algorithms as well as new measurement functions, with optimum performance characteristics, are identified, designed and implemented. The availability and development of fast and accurate machine learning models and metrology inference strategies is essential.
 

As a Mathematics Machine Learning Engineer you will be doing the following:

• Development of models for scanners and optical metrology solutions with statistically correct parameter inference, deep learning and optimization algorithms, and system calibrations, to improve semiconductor high-volume fab control solutions.
• Implement machine learning and deep learning scanner models and metrology applications, with a mindset for scalable data-intensive and distributed software architectures, at the interface with colleague physics, data science, and software groups at ASML.
• Communicate crystal clearly on physical principles, mathematical solutions and design decisions to stakeholders, without omitting the essentials.
• Drive for data and code quality, and collaborate and help to implement along industry coding best practices.
• Work as a team with similar-minded people, benefitting from each other’s specific competences.
• Design and realize fully functional proof-of-concept subsystems on the edge of system specifications, costs and project planning, thereby contributing directly to products for B2B customers world-wide.
• Review technical analyses from the team, and structure team contributions keeping the overview.
• Consolidate technical-team identity in communication with other departments.
• Contribute to technical product roadmaps and generate intellectual property protecting ASML products, while developing the best metrology solutions and a well-founded vision on semiconductor metrology.
• Keywords: Mathematical optimization, optical modeling and calibration, mathematics, inverse problem, parameter inference, (non-)convex optimization, machine learning, deep learning, neural network, regression, information theory.

Education and experience

• Ph.D. in Applied Mathematics, Physics, Computer Science, or Electrical Engineering.
• 0-3 year of working experience.

Skills

• Excellence in numerically stable modeling and inference strategies, using sound physical-mathematical principles and insights
• Ability to explain physical principles and algorithmic solutions in a crisp way, without omitting the essentials
• Excellence in numerical mathematics, and affinity with machine learning methods, data-intensive and distributed software architecture (cloud) as environment for metrology applications
• Drive for structuring the scripting code base in the cluster, and be energized by helping colleagues in this
• Fluency in the languages Python, C++, MATLAB, or Julia, and awareness of compatibility with other software
• Sound understanding of the fundamentals such as optics, linear algebra, probability theory, robust optimization and (deep) learning methods
• Drive creative solutions within the bigger picture, with the product and customer in mind
• Initiating, self-propelling and decisive in an ambiguous environment
• Team worker, and ability to influence without power
• Pragmatic approach and pro-active attitude, with result focus and a ‘can do’ spirit

Other

We would like to receive a letter of motivation in which it is clearly stated how you have applied your knowledge and experience in practice. 

Please note that applications will only be reviewed starting in week 35 due to the holiday period