Data, 3D Reconstruction & Generative AI: Master Thesis Projects 2027, Cluster B

Your Mission

Every Cluster B thesis combines research with practical experimentation. You will work with real-world data, develop and evaluate methods, analyze results, and contribute insights that may help shape future AI and autonomous-driving systems.

You will have significant ownership of your work while receiving regular guidance from experienced engineers and researchers. Together, you will define a realistic scope, discuss technical choices, and adapt the direction as new evidence emerges.

Who You Are

You are curious about how AI systems learn from and represent the physical world. You enjoy exploring challenging technical questions, testing ideas, and learning from results, including when they do not match your initial expectations.

You are currently pursuing a master’s degree in Computer Science, Machine Learning, AI, Robotics, Electrical Engineering, Engineering Physics, Applied Mathematics, or a related field.

We value different perspectives, experiences, and ways of approaching problems. You do not need to match every item below. We are interested in your technical foundation, learning ability, motivation, and potential to grow during the thesis.

What You Bring

• Programming experience, preferably in Python

• Academic or practical experience in machine learning, deep learning, computer vision, data analysis, geometry, or a related area

• Ability to read and evaluate scientific literature

• Ability to communicate technical reasoning and results in English

• Eligibility to complete the thesis as part of a university-level course

Your thesis must be approved by a university examiner and supervisor and completed as part of a university-level course.

Helpful, but Not Required

Depending on the project, experience in one or more of the following areas may be useful:

• Deep learning frameworks such as PyTorch

• Foundation models, self-supervised learning, or generative AI

• Computer vision, LiDAR, point clouds, or 3D perception

• Sensor calibration, neural rendering, or 3D Gaussian Splatting

• Data pipelines, GPU computing, or large-scale experimentation

• Research projects, publications, or open-source contributions

Previous experience with the exact method named in your preferred project is not required. We encourage you to apply if you have relevant foundations and are motivated to learn.