Sr. Deep Learning Scientist - Cardio Imaging

Siemens Healthineers
Location: 
Princeton, NJ
Job Type: 
Full Time
Closing Date: 
Sunday, December 31, 2017

Sr. Deep Learning Research Scientist – Cardiovascular Imaging - 202954

 

Job Description

The Medical Imaging Technology team of Siemens Healthcare Technology Center has an immediate opening in Princeton, NJ for a research scientist with a focus on Deep Learning and Medical Image Analytics. Our Princeton facility is recognized for providing a stimulating environment for highly talented and self-motivated researchers. You will have the opportunity to test your knowledge in a challenging problem-solving environment. You will be encouraged to think out-of-the-box, innovate and find solutions to real-life problems. Our team has a strong publication record in leading journals and conferences.

What are my responsibilities?

• Research, design, and implement algorithms in Deep Learning for Medical Image Analysis problems.
• Advance the state-of-the-art , including generating patents and publications in top journals and conferences.
• Work on large-scale, real-world problems.
• Fast prototyping, feasibility studies, specification and implementation of image and video analysis product components.
• Work with customers to understand requirements and deliver solutions

What do I need to qualify for this job?

• Ph.D. in an engineering or science field such as Computer Science, Electrical Engineering, Statistics, or Applied Math
• Strong background in Deep Learning.
• Graduate research and internship experience in Computer Vision, Machine Learning and Image Understanding preferred.
• Experience in GPU programming.
• Hands-on coding skills and ability to quickly prototype in C++ is a must. Further experience in Scripting languages such as Python is a plus.
• Outstanding written and verbal communication skills in English are required
• Excellent interpersonal skills and a can-do attitude
• Strong collaboration skills and ability to thrive in a fast-paced environment.
• Successful candidate must be able to work with controlled technology in accordance with US Export