Mandatory Internship in software-defined medical imaging data acquisition and reconstruction systems

Job Description

Mode of Employment: 
Fixed Term / Full-time / 

Do you want to help create the future of healthcare? Our name, Siemens Healthineers, was selected to honor our people who dedicate their energy and passion to this cause. It reflects their pioneering spirit combined with our long history of engineering in the ever-evolving healthcare industry.
We offer you a flexible and dynamic environment with opportunities to go beyond your comfort zone in order to grow personally and professionally. Sound interesting?

Then come and join our global team as Working Student (f/m/d) and take a unique opportunity to participate in the development of next generation software-defined medical imaging data acquisition and reconstruction systems of Siemens Healthineers.

Your tasks and responsibilities:

  • In collaboration with internal and external partners, the core technologies of our future data acquisition and reconstruction systems are evaluated and rated. All tasks at a glance:
  • Prototype implementation and performance characterization of deep learning-based image reconstruction algorithms for real-time medical applications
  • Systematically measure and compare performance of different solutions
  • Prototype implementation on x86 and ARM based system architectures
  • Investigate technologies for receiving and processing input data via Ethernet for real-time DNN inference at data rates up to 100 Gbit/s

To find out more about the specific business, have a look at  

Your qualifications and experience:

  • You are a Master student (f/m/d) of medical/electrical/computational engineering, computer science or relevant subjects
  • You have a profound understanding of signal processing, data analysis, deep learning and/or Hardware/GPU acceleration, CUDA/GPU parallel computing
  • Your understanding of Linux networking and memory management is very good
  • Basic knowledge in data transport acceleration technologies like RDMA, RoCE, GPUDirect
  • You bring sound knowledge and experience with C, C++, CUDA, Python
  • You are experienced in working with machine learning frameworks, in particular TensorFlow and TensorRT

Your attributes and skills:

  • Very good English knowledge completes your profile

The internship will be full-time for 6 months and can be combined with a master thesis opportunity.

Our global team:

Siemens Healthineers is a leading global medical technology company. 50,000 dedicated colleagues in over 70 countries are driven to shape the future of healthcare. An estimated 5 million patients across the globe benefit every day from our innovative technologies and services in the areas of diagnostic and therapeutic imaging, laboratory diagnostics and molecular medicine, as well as digital health and enterprise services.

Our culture:

Our culture embraces different perspectives, open debate, and the will to challenge convention. Change is a constant aspect of our work. We aspire to lead the change in our industry rather than just react to it. That’s why we invite you to take on new challenges, test your ideas, and celebrate success.

Check our Careers Site at

As an equal opportunity employer, we welcome applications from individuals with disabilities.

Wish to find out more before applying? Contact us: Students@Siemens Healthineers

We care about your data privacy and take compliance with GDPR as well as other data protection legislation seriously. For this reason, we ask you not to send us your CV or resume by email. We ask instead that you create a profile in our talent community where you can upload your CV. Setting up a profile lets us know you are interested in career opportunities with us and makes it easy for us to send you an alert when relevant positions become open. Click here to get started.

Siemens Healthineers Germany was awarded the Great Place to Work® certificate.

Organization: Siemens Healthineers

Company: Siemens Healthcare GmbH

Experience Level: Student (Not Yet Graduated)

Job Type: Full-time

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