Your Mission 

Are you passionate about artificial intelligence and medical technology? During this internship, you will help develop Computer Vision and Machine Learning solutions for next-generation medical navigation technologies. You will work with real-world image and sensor data, contribute to innovative development projects, and collaborate with experienced engineers in an international MedTech environment. This opportunity allows you to strengthen your technical expertise, gain practical experience with advanced AI technologies, and contribute to innovations that support future computer-assisted and robot-assisted surgical procedures. 

What You'll Do 

• Develop and evaluate prototypes and algorithms using Computer Vision and Machine Learning methods. 

• Analyze image and sensor data to improve the performance of medical navigation solutions. 

• Apply Deep Learning, OpenCV, and related technologies to technical challenges. 

• Support research and development activities for innovative medical technology applications. 

• Collaborate with multidisciplinary engineering teams to deliver impactful project outcomes. 

What You'll Bring 

• Currently pursuing a Master's degree in Electrical Engineering, Computer Science, Physics, Medical Engineering, Microsystems Engineering, or a related field. 

• Initial experience with Python programming. 

• Basic knowledge of Computer Vision, Machine Learning, or image analysis techniques. 

• Initial exposure to OpenCV, PyTorch, or comparable development frameworks. 

• Very good English skills (B2) or higher. 

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What's In It for You 

• Paid six-month full-time internship (voluntary internship alongside studies, mandatory internship, or a combination of both possible) 

• Start date: March or April 2027 

• Location: Freiburg (Breisgau, Germany) 

• Flexible working hours and attractive holiday allowance 

• Mentoring and training to support your growth 

• Hands-on projects with real-world impact in an international environment 

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Application Process 

Please upload all application documents (cover letter, current CV/resume, and transcript of records) either as one combined file or as separate files in the “Resume/CV” section of the application form. Additional documents cannot be uploaded later. 

Our selection process is simple and transparent: 

1. Application review 

2. Short introductory phone conversation with our university talent acquisition team 

3. One-hour video interview with the hiring team 

We value diversity and are committed to creating an inclusive workplace. We welcome applications from all qualified individuals, regardless of ethnic, national, or social background, gender, disability, age, or sexual identity.