Intern automation and computer vision in microscopy (f/m/x)

München

ZEISS Group

ZEISS is an international leading technology enterprise operating in the optics and optoelectronics industries. 

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Your role

In this role you will be part of a team called ZEISS Solutions Lab. You will build customized automation solutions for ZEISS customers that need support for special light microscopy applications. Typically, these solutions encompass a completely automated light microscopy workflow from image acquisition to the image analysis. Applications usually come from quality labs in the pharma, manufacturing or electronics industry, although, use cases may arise from any business area. Together with our data scientists you will implement full HW automation and computer vision pipelines ranging from microscope control to the preprocessing of images to the detecting and segmenting of objects and the final statistical analysis. You will have the possibility to improve your knowledge in many areas, such as the application of deep learning models and image processing algorithms or in advanced software engineering topics.

Your profile
  • Strong proficiency with Python and the scientific ecosystem (e.g. numpy, matplotlib, pandas)

  • Taken classes in the fields of computer science, mathematics, physics or any other STEM subject with advanced programming skills

  • Successfully completed at least 2 years of study

  • Fluent English or German

  • Deep curiosity and willingness to dive into new subject areas

  • Ability to work in an independent, goal- and output-oriented manner

  • Good communication skills in order to work with our customers

  • Fun working in agile, interdisciplinary teams The following will be considered an advantage:

  • Familiarity with computer vision packages for Python, such as scikit-image

  • First experience with light microscopes

  • First experience with machine learning

Your ZEISS Recruiting Team:

Laura Steininger
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Job stats:  19  9  0

Tags: Agile Computer Science Computer Vision Deep Learning Engineering Machine Learning Mathematics Matplotlib NumPy Pandas Pharma Physics Pipelines Python Scikit-learn Statistics STEM

Region: Europe
Country: Germany

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