Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Deep learning-based image analysis to investigate early embryo development
Using state-state-of-the art deep learning models, we will analyse early embryo development with the goal to understand the principles of self-organisation.
Keywords: embryo development, image segmentation, neural networks, deep learning
The student will use state-state-of-the art deep learning models to segment the individual cells in three dimensional images of early embryos. There will be a particular focus to distinguish cell populations based on morphological features. Through this project, the student will gain familiarity with applying deep learning to analyze microscopy images and fundamentals of data science.
The project would suit a student with previous experience in writing scripts and a keen interest in applying deep learning approaches to image processing. Previous experience with image processing is helpful, but not required. The project length is 12 weeks, but can be shorter in case of prior experience with image processing.
The student will use state-state-of-the art deep learning models to segment the individual cells in three dimensional images of early embryos. There will be a particular focus to distinguish cell populations based on morphological features. Through this project, the student will gain familiarity with applying deep learning to analyze microscopy images and fundamentals of data science. The project would suit a student with previous experience in writing scripts and a keen interest in applying deep learning approaches to image processing. Previous experience with image processing is helpful, but not required. The project length is 12 weeks, but can be shorter in case of prior experience with image processing.
The goal is to develop deep learning models to distinguish cell populations based on morphological features.
The goal is to develop deep learning models to distinguish cell populations based on morphological features.
Prof Dagmar Iber (iberd@ethz.ch) or Dr Kevin Yamauchi (kevin.yamauchi@bsse.ethz.ch) ETH Zurich, Computational Biology (CoBi)
Prof Dagmar Iber (iberd@ethz.ch) or Dr Kevin Yamauchi (kevin.yamauchi@bsse.ethz.ch) ETH Zurich, Computational Biology (CoBi)