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PhD Thesis Algorithms (Numerical Modelling) in Semiconductors (f/m/x)
Carl Zeiss SMT GmbH, Jena

PhD Thesis Algorithms (Numerical Modelling) in Semiconductors (f/m/x)

Shape the digital future of ZEISS in the Global Algorithms & Software Semiconductor Mask Solutions team! 

Your sophisticated algorithms evaluate enormous amounts of image data in a highly efficient manner. Only in this way do we as a team enable the performance of our complex photomask systems - the essential building block to manufacture defect-free microchips. 

 

Your Role 

Our high-tech measurement systems for semiconductor lithography are characterized by extreme demands on measurement performance. This performance is essentially achieved by optical components at the highest level. Nevertheless, remaining manufacturing tolerances lead to almost insurmountable accuracy limits. Breaking through these limits using algorithms is your new challenge at ZEISS. 

 

You will research the architecture and functionality of highly accurate mathematical-optical models and their numerical solutions in expert teams. Your tasks will range from conceptual design to implementation and performance evaluation. Product maturity and value adding algorithms are your success. You continuously expand your network within the company and are in close contact with our research partners. 

 

Your innovation-driven mindset allows you to keep an eye on the latest technical developments and publications. You and your colleagues thus create the conditions for excellence.

Your Profile 

  • Fascinated and excellently trained for designing numerical simulation methods and optimization techniques - ideally for solving inverse problems 

  • Very good physical and methodological knowledge of geometrical and physical optics or transferrable domains 

  • A strong will and practical programming experience with either: Python, C++, Matlab, preferably also with parallel computing (CUDA, Cloud, MPI) 

  • A very successfully completed degree in physics, computer science, engineering, or applied mathematics 

  • (optional) FOSS contributions  

  • (optional) Experience in classical data processing and (scientific) machine learning  

  • (absolutely mandatory 😉) Great passion to successfully solve complex and difficult problems independently and in a team 

  • You have read this, and other, papers: AIMS EUV evolution towards high NA: challenge definition and solutions implementation (spiedigitallibrary.org) 

Your ZEISS Recruiting Team:

Kathrin Siegel
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