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