Job Opportunity:
Are you passionate about computer vision and deep learning? We are seeking a highly motivated and skilled individual to contribute to our team as a Master Thesis Project researcher. This is an excellent opportunity to apply your knowledge and skills in object detection architectures, anomaly detection, and real-world industrial applications.
About the Role
This thesis project involves exploring alternative state-of-the-art object detection architectures for anomaly detection in tire images. You will implement and evaluate these models on a dataset specifically designed for detecting anomalies in real-world scenarios. Your goal will be to assess the performance of object detection architectures in identifying and localizing anomalies, providing insights into their suitability for real-world industrial applications.
Key Responsibilities
* Design and implement object detection architectures for anomaly detection
* Evaluate the performance of these architectures using metrics such as mean Average Precision (mAP) and Intersection over Union (IoU)
* Analyze the impact of IoU-based loss functions on the performance of object detection architectures
Requirements
To be successful in this role, you should have a strong background in computer science, artificial intelligence, data science, or a related field. You should also have good understanding of computer vision and deep learning concepts, knowledge of machine learning algorithms and evaluation metrics, and proficiency in Python and deep learning frameworks.
What We Offer
* A challenging and international work environment
* Collaborative working style
* Learning opportunities for professional development
How to Apply
If you are interested in this exciting opportunity, please submit your application with your CV and a cover letter outlining your qualifications and experience.