System Developer Algorithms (Image Processing) - Semiconductor Technology (m/f/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 requirements on measurement performance and a high level of automated image evaluation.
Robustly and efficiently processing large amounts of data with machine learning comes with many challenges but also generates high value for our customers.
your role is to bring such solutions into our products
you will develop highly accurate image processing algorithms and machine learning based solution in an interdisciplinary team of experts
your tasks will range from conceptual design and state-of-the-art analysis over implementation and performance evaluation to final product maturity
within an agile team, you turn ideas and data into digital products
your innovation-driven mindset allows you to keep an eye on the latest technical developments and publications
you will continuously expand your network within the company and our research partners
you and your colleagues thus create the conditions for excellence
Your Profile
very good knowledge of machine learning and classical image processing as well as good physical and methodological knowledge of geometric and physical optics
intensive practical programming experience with : Python, Matlab, possibly C++, preferably also with parallel computing (CUDA, Cloud systems)
very successfully completed degree in physics, computer science, engineering, or applied mathematics (PhD preferred)
great passion to successfully solve complex and difficult problems independently and in a team
very good German and English skills
Your ZEISS Recruiting Team :
Kathrin Siegel
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