Master Thesis Project: Computer Vision

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Endeavoring to close the gap between Academy and Industry, we are opening Curricular Internship Positions for Master Students that want to develop their Master Thesis in a company environment.
All our Research Positions: · Are Paid Positions.
· Are open for Hybrid or Remote Work.
· Will emerge you in an Innovative & Young Culture.
· Allow you to work on Real World Applications.
· Focus on developing Cutting Edge Solutions.
Requirements: Background in Computer Science, Data Science, Mathematics, or a related field.
Strong Python programming skills.
Practical experience with Machine Learning libraries (e.G., PyTorch, TensorFlow) and image processing tools (e.G., OpenCV, scikit-image).
Ability to work collaboratively in a multidisciplinary, fast-paced environment.
Fluent in English, both written and spoken.
If you do not find below a project that you would like to work on, feel free to contact us, regardless.
We may have other better suited opportunities for you.
Similarly, we are always interested to listen if you have a project idea that you would like to implement collaboratively with us.
--------------------------------------------------------------------------------------- In machine vision environments—especially those deployed in industrial or high-throughput inspection systems—consistency in image quality is non-negotiable.
Any sudden change in lens focus, lighting conditions, or unexpected objects entering the field of view (FOV) can compromise the reliability of downstream AI models and disrupt critical operations.
We are seeking a Master Thesis Student with a strong interest in Machine Learning and Computer Vision, who will explore anomaly detection techniques tailored to identifying such real-time issues in image acquisition systems.
This thesis will be carried out as a curricular internship and will contribute to improving the operational resilience and reliability of visual inspection pipelines.
Main Objectives: Research state-of-the-art anomaly detection methods in imaging pipelines.
Design and test machine learning approaches that flag capture-related degradations (e.G., blur, lighting drift, occlusions).
Simulate and model real-world degradation scenarios to evaluate algorithm robustness.
Integrate anomaly detection into a live image capture system.
Document findings and provide a detailed final report, including implementation insights and suggestions for future work.
--------------------------------------------------------------------------------------- Object detection systems are increasingly being deployed in complex and uncontrolled environments, where objects can appear anywhere within the image.
However, some detection pipelines exhibit positional bias, performing better when objects are centered or conform to training distributions.
We are looking for a Master Thesis Student to investigate how AI models can be made more robust to object location within an image