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Vacature Yieldstar Statistical Learning and Physical Model Design Engineer


Regio:
Noord-Brabant
Ervaring:
Junior
Opleiding:
PhD

Functieomschrijving Yieldstar Statistical Learning and Physical Model Design Engineer

Introduction
Do you have the ambition to work for one of the most prestigious suppliers of the semiconductor industry ? Do you enjoy solving algorithm design problems under demanding time, accuracy, and memory requirements ? Do you like to use your creativity, your in-depth knowledge of the machine learning principles, and your hands-on experience with practical problem solving, being part of a highly talented group of physicists, applied mathematicians, information theorists, and (electromagnetic) simulation experts ?

Sector Information
Within ASML the sector Development & Engineering is responsible for the development, specification and design of new ASML products. The Business Line Applications provides integrated solutions with computational, metrology and control technology. These solutions extend and improve the performance of lithography products for the semiconductor industry.

Job Mission
Within D&E Applications, the group YieldStar Algorithms and Physical Modeling covers the development of physical, optical and mathematical models and methods required to infer physical model parameters from optical scatterometry data. Relevant new metrics and algorithms, as well as new measurement functions, with optimum performance characteristics using the raw acquisitions are identified, designed and implemented.
The group secures both the Scatterometry Modeling Competency and the Applied Mathematics for Parameter Estimation Competency.

Job Description

  • Propose solutions for statistically correct parameter inference, physical models and calibrations, which enable and improve semiconductor metrology solutions beyond the optical resolution limit.
  • Communicate crystal clearly on the mathematical principles, algorithm solutions and physical models 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.
  • Interfacing with the Research and on-product applications groups, while developing the best metrology solutions and a well-founded vision on semiconductor metrology.
  • Contribute to technical product roadmaps and generate intellectual property protecting ASML products.
  • Working as a team with similar-minded people, benefitting from each other’s specific competences.
  • Keywords: parameter inference, deep-learning, multi-dimensional output GP’s, inverse problems, regularization, physical calibration, optimization, applied statistics, machine learning, pattern recognition and information theory.

Functie-eisen

Ph.D. in Electrical Engineering, Physics, Applied Mathematics, or Computer Science

Experience
Established experience in mathematical and physical modeling, (big) data analysis and algorithm design

Personal skills

  • Excellence in physical modeling, optimization algorithms, and code development
  • Experienced in inference techniques to develop large scale and (numerically) stable solutions
  • Ability to explain complex physical models and algorithms in a crisp way, without omitting the essentials
  • Sound understanding of the fundamentals such as linear algebra, probability theory, (non-) parametric Bayesian methods
  • Drive creative solutions -within the bigger picture- with the product and customer in mind
  • Decisive and self-initiating in an ambiguous environment
  • Ability to influence without power
  • Team worker
  • Pragmatic approach and pro-active attitude, with result focus and a ‘can do’ spirit

Education: Ph.D. in Electrical Engineering, Physics, Applied Mathematics, or Computer Science and required experience:

  • Physical modeling, optimization algorithms, and code development
  • Inference techniques to develop large scale and (numerically) stable solutions

Skills:

  • Ability to explain complex physical models and algorithms in a crisp way, without omitting the essentials
  • Sound understanding of the fundamentals such as linear algebra, probability theory, (non-) parametric Bayesian methods

Locatie

Eindhoven


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