30+ days ago - req30559
Physics Machine Learning Engineer
Research & development
In a nutshell
Research & development
Introduction to the job
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 and machine learning pipelines, and your hands-on experience with practical problem solving, being part of a highly talented group of algorithm experts ?
Role and responsibilities
-Develop optical metrology solutions with statistically correct parameter inference, machine learning and optimization algorithms, and system calibrations, to improve semiconductor metrology and enable high-volume fab control solutions.
-Implement machine learning and deep learning metrology applications, with a mindset for scalable data-intensive and distributed software architectures, at the interface with colleague data science, functional and software groups at ASML.
-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.
-Communicate crystal clearly on physical principles, algorithm solutions and design decision to stakeholders, without omitting the essentials.
-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.
Education and experience
Ph.D. in Physics, Computer Science, Electrical Engineering, or Applied Mathematics
Working at the cutting edge of tech, you’ll always have new challenges and new problems to solve – and working together is the only way do that. You won’t work in a silo. Instead, you’ll be part of a creative, dynamic work environment where you’ll collaborate with supportive colleagues. There is always space for creative and unique points of view. You’ll have the flexibility and trust to choose how best to tackle tasks and solve problems.
To thrive in this job, you’ll need the following skills:
- Excellence in numerically stable modeling, code development, using sound physical principles and insights
- Ability to explain physical principles and algorithmic solutions in a crisp way, without omitting the essentials
- Affinity with 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, Julia, MATLAB, or C++, and awareness of compatibility with other software
- Sound understanding of the fundamentals such as optics, linear algebra, probability theory, stochastic programming, 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
Diversity & Inclusion
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 diversity and inclusion is a driving force in the success of our company.
Keywords: deep learning pipeline, data-intensive and distributed computing, cloud computing, data processing, parameter inference, (non-)convex optimization, physics, software, dataflow, robust (un)supervised and reinforcement learning, neural network, inverse problem, physical calibration, mathematics, optics, regression, information theory.
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