Sr. Research Scientist - Image Guided Interventions Technologies
Business Unit: Strategy & Innovation
Requisition Number: 217954
Primary Location: United States-New Jersey-Princeton
Assignment Category: Full-time regular
Experience Level: Senior level
Education Required Level: Doctorate Degree
Travel Required: No
Siemens is a global technology powerhouse that has stood for engineering excellence, innovation, quality, reliability and internationality for more than 165 years. As a global technology company, Siemens is rigorously leveraging the advantages that this setup provides. To tap business opportunities in both new and established markets, the Company is organized in nine Divisions: Power and Gas, Wind Power and Renewables, Energy Management, Building Technologies, Mobility, Digital Factory, Process Industries and Drives, Healthineers and Financial Services.
With 45,000 employees Siemens Healthineers is one of the world’s largest suppliers of technology to the healthcare industry and a leader in medical imaging, laboratory diagnostics and healthcare IT. All supported by a comprehensive portfolio of clinical consulting, training, and services available across the globe and tailored to customers’ needs. So that more people can have a life that is longer, richer, and more filled with happiness.
For more information, please visit: http://www.usa.siemens.com/healthineers
Sr. Research Scientist - Image-Guided Interventions Technologies
The Medical Imaging Technologies team of Siemens Healthcare Technology Center has an immediate opening in Princeton, NJ for a research scientist with a focus on medical image analysis for image-guided interventions and surgery, including image registration/fusion, real-time computation, x-ray imaging, and artificial intelligence (deep learning/deep reinforcement learning). 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 high-impact solutions to real-life, healthcare problems. Our team has a strong publication record in leading journals and conferences.
What are my responsibilities?
+ Research, design and implement disruptive algorithms for image-guided therapies and interventional imaging.
+ Advance the state-of-the-art in the field, 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 medical imaging product components.
+ Work with customers to understand algorithm and workflow requirements and deliver solutions
What do I need to qualify for this job?
+ Ph.D. in Computer Science, Biomedical Engineering, Electrical Engineering, Statistics or Applied Mathematics.
+ 3-5+ years of experience (including Ph.D. study) in medical image analysis, machine learning (Deep Learning, Deep Reinforcement Learning) or real-time image analytics and modeling, with focus on real-time interventional imaging (e.g. x-ray)
+ Knowledge in biomechanical modeling and solvers is a plus
+ Strong theoretical and practical background, with successful demonstration of key responsibilities
+ Proven ability to develop new research ideas as demonstrated by a strong publication record and early developments to the level of a working system prototype
+ Strong coding skills and ability to quickly prototype in C++ with proven track record. Further experience in GPU and scripting languages such as Python is a plus.
+ Entrepreneurial approach who is willing to accept challenges and responsibilities
+ Excellent interpersonal skills and a can-do attitude, with strong collaboration skills and ability to thrive in a fast-paced, global environment
+ Adaptability to work in a growing, dynamic team across countries
+ Outstanding written and verbal communication skills in English is required
Keywords: “biomechanical modeling”, “x-ray”, “interventional imaging”, “deep learning”, “machine learning”