Work in a global R&D team to research, design and develop image analysis related solutions with artificial intelligence, especially deep learning, machine learning and big data analysis.
- Research and develop image processing related solutions to solve clinical problems based on new techniques.
- Design and fast prototyping Computed Tomography (CT) clinical applications for global market to support clinical research and improve clinical routine work.
- Design and fast prototyping image analytics algorithms or applications for interactive workflow to support clinical research and improve clinical routine work
- If necessary, work with clinical partners to understand the requirements and feedback for further improvement.
- Design, and implement algorithms in Deep Learning for Medical Image Analysis problems
- Work on large-scale data sets and real-world problems
- Close collaboration with Siemens Healthineers Business lines to understand requirements and deliver successful AI solutions
- Data analysis of clinical studies with clinical partners
- Ph.D. preferred in an engineering or science field such as Computer Science, Electrical Engineering, Statistics, or Applied Math. Alternative MSc. in an engineering or science field such as Computer Science, Electrical Engineering, Statistics, or Applied Math with at least 3 years’ experience
- 2+ years industry experience in building medical imaging solutions.
- Strong background in Deep Learning and familiar with TensorFlow, PyTorch and etc.
- Proficiency in Python and ability to quickly prototype in C++ is desired
- Graduate research and internship experience in Computer Vision, Machine Learning and Image Understanding preferred
- Experience in medical image and medical big data preferred
- Outstanding written and verbal communication skills in English are required
- Excellent interpersonal skills
- Strong collaboration skills and ability to thrive in a fast-paced environment
Organization: Siemens Healthineers
Company: Siemens Shanghai Medical Equipment Ltd.
Experience Level: Early Professional
Full / Part time: Full-time