Working Student/Internship - AI-Based Medical Image Analysis

About Snke

Established in 2020 and based in Munich, Snke is pioneering advancements in health technology through scalable AI and big data innovations. Specializing in expansive digital health platforms and software-driven medical devices, Snke serves healthcare providers, registries, and agencies by delivering dependable orchestration layers that facilitate safe, effective interventions and improve patient care. Our presence extends globally with teams in Chicago, Heidelberg, San Diego, and Tel Aviv, fostering integrated solutions that promote meaningful improvements in healthcare delivery.

Role Overview

Join our Research Platform team as a working student or intern focusing on the application of deep learning models to analyze medical images. Your efforts will center on assessing and developing AI techniques for specific use cases such as CT phase recognition (including native, arterial, venous, and delayed phases), general characterization of medical images, and automatic detection of anatomical regions represented in scans. You will evaluate state-of-the-art methods, experiment with pre-existing and custom-trained models, and measure their performance on genuine radiological datasets, contributing directly to enhancing our research platform and streamlining clinical workflows for physicians.

Key Responsibilities

• Collaborate closely within a skilled, motivated team under dedicated mentorship.
• Implement, test, and benchmark current deep learning methodologies applied to medical imaging tasks.
• Compile and communicate your research findings to both technical experts and clinical stakeholders.
• Work hands-on with real patient imaging data within a regulated framework.

Qualifications

• Currently enrolled in Computer Science, Medical Informatics, Data Science or a related discipline.
• Practical experience programming in Python and familiarity with deep learning libraries such as PyTorch or TensorFlow.
• Foundational knowledge of machine learning and neural networks, with some exposure to computer vision concepts.
• Keen interest in medical imaging and a strong desire to learn and contribute.
• Experience with version control tools like Git is advantageous.
• Interest or knowledge of medical data standards and protocols including DICOM, gRPC, and FHIR is beneficial.
• Good proficiency in English is required; German language skills are a bonus.

Why Join Snke

• Engage in impactful work advancing medical technology with lasting effects.
• Flexible scheduling that complements your academic commitments, including options for remote work.
• Gain insight into the development process of actual medical devices.
• Join a supportive team environment with social and company events.
• Opportunity to orient your academic thesis (Bachelor’s or Master’s) around your contributions.
• Access personalized growth opportunities and mentoring support.