ImFusion GmbH

Computer Vision Master's Thesis for Point Tracking

Stellenbeschreibung:

Your mission

Working Student (MSc) – Join the innovation wave at ImFusion!

Hey computer vision enthusiasts!

Are you currently enrolled in an MSc program and able to work in Munich? We are looking for a Master’s student in the topic of computer vision, deep learning and synthetic data generation, for surgical applications in our computer vision team.

At ImFusion, we are not just developing software – we are pushing the boundaries of medical imaging, AI and robotics. Our solutions help clinicians in their day‑to‑day work and improve patient care. We are looking for working students to help us take our applied research to the next level.

What you'll be up to:

Testing, adaptation and integration of the state‑of‑the‑art methods and frameworks.

Your profile

What we're looking for:

  • Enrolled in a Master's program with coursework in computer vision and machine learning (please upload your master's transcript using the certificate upload field in the application form).
  • A strong theoretical background in computer vision, 3D geometry, and machine learning.
  • Ideally, prior experience with the aforementioned topics.
  • Proficiency in Python, with expertise in PyTorch and other relevant computer vision libraries.
  • Good to have: prior experience in synthetic data generation.
  • Enthusiasm to spend a minimum of six months at our company.
  • Fluency in English.
  • A quick learner and a great team player.

Why us?

Why you'll love working with us:

  • A dynamic international team in a flat‑hierarchy environment.
  • The satisfaction of working on products that actually improve patient lives.
  • Competitive salary
  • Startup agility with the stability of an established company.
  • Fun team events throughout the year.

Sound like your kind of challenge? Let’s talk! Drop us your application, and let’s build the future of medical imaging together.

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DeutschDE: Bitte erwähne Fuchsjobs, als Quelle Deiner Bewerbung

Stelleninformationen

  • Veröffentlichungsdatum:

    18 Feb 2026
  • Standort:

    München
  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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