Research Scientist

in Digital Pathology

Shanghai / San Diego


We are seeking digital pathology scientists with experience in developing image analysis algorithms to support translational research, biomarker development and discovery biology. You will help create and implement the next generation of knowledge discovery tools, by developing cutting-edge machine learning algorithms to extract decision making signals from digital brightfield and fluorescence images of FFPE fixed patient tumor samples. Our group routinely collects multi-terabyte multiplex image data sets and the successful candidate will contribute to the design and building of production image acquisition/analysis workflows capable of interacting with sets of tumor sample images at scale. The position will require experience in the rapid prototyping and testing of existing/novel convolutional neural network designs.

We are looking forward to your application at!


  • Evaluate existing and emerging convolutional machine learning approaches;

  • Develop and testing proof-of-concept algorithm prototypes;

  • Deliver production quality customized analysis algorithms with appropriate QC visualization workflows;

  • Work with our pathologists, scientific end-users, mathematicians and statisticians to improve and refine existing analysis and visualization tools.


  • Python, Matlab or R programming skills and experience using relevant libraries (e.g. numpy, scipy, openCV, scikit-image, scikit-learn, etc.);

  • Distributed computing experience either on local cluster or using cloud services;

  • Experience in design and optimization of convolutional neuronal networks. Ideally in the image analysis domain;

  • Knowledge of state of the art machine learning algorithms (e.g. Bayesian learning, convnets) graph analysis (e.g. random walk, gradient decent) and statistical modeling (e.g. hypothesis testing, dimensionality reduction);

  • Proficiency in standard data analytics toolkits in Python and/or R

  • BONUS: Strong background in computer science and or related field;

  • BONUS: Experience with light-microscopes or their digitized images;

  • BONUS: Deep understanding of current machine learning / deep learning technologies with image analysis/computer vision application;

  • BONUS: Experience with deep learning frameworks (TensorFlow, PyTorch, Caffe, etc).

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