Job
In dieser Thesis entwickelst du Methoden zur Bewertung synthetischer Datensätze basierend auf aufgabenrelevanten Informationen und arbeitest an der Verbesserung des Sim-to-Real Transfers in der industriellen Computer Vision.
Job-Technologien
Deine Rolle im Team
- Would you like to explore a fundamental research question at the intersection of representation learning, synthetic data, and industrial computer vision? In this thesis, you will investigate how to determine whether a synthetic dataset contains the information needed to train a model for use in real-world applications.
- Common dataset comparison metrics primarily measure visual or statistical similarities. However, they do not necessarily capture whether the shared information is relevant to a specific task, such as object detection. Existing methods for assessing transferability also often assume that a suitable pretrained representation is already available. This is not always the case, particularly in specialized industrial applications.
- The goal of this thesis is to learn and evaluate latent representations that allow synthetic and real-world datasets to be compared based on their task-relevant information.
- Investigate supervised and self-supervised approaches to learning task-aware representations from labeled synthetic data and unlabeled or partially labeled real-world data.
- Develop distance metrics in latent space to compare datasets and evaluate how well these metrics predict downstream model performance in real-world applications.
- Compare your approach with established methods, analyze its limitations, and implement a prototype for assessing synthetic datasets.
- Validate your findings experimentally and investigate the underlying assumptions and potential failure cases.
- Document your findings and derive recommendations for using and selectively improving synthetic datasets in industrial AI development.
- The outcome of your thesis will be a validated methodology and a functional prototype for task-aware synthetic data assessment, together with a critical analysis of the underlying assumptions, limitations, and potential failure cases. Your findings could help improve the selection, reuse, and refinement of synthetic datasets.
Unsere Erwartungen an dich
Ausbildung
- Currently pursuing an advanced master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Robotics, Physics, Engineering, or a related field.
Qualifikationen
- Knowledge of representation learning, self-supervised learning, or synthetic data is an advantage.
- Ability to understand scientific literature, formulate hypotheses, design controlled experiments, and critically evaluate results.
- Ability to quickly grasp complex technical problems and work independently and systematically.
- Ability to communicate findings clearly and collaborate with an interdisciplinary team.
- Strong interest in representation learning, synthetic data, sim-to-real transfer, and applied industrial AI research.
- Good written and spoken English skills.
Erfahrung
- Good knowledge of machine learning and deep learning, with practical experience in computer vision and neural networks.
- Good Python programming skills and practical experience with PyTorch or a comparable deep learning framework.
Unser Angebot
- Summer Semester 2027 - fixed term of 3-6 months.
Benefits
Mehr Netto
Essen & Trinken
Work-Life-Integration
Gesundheit, Fitness & Fun
Themen, mit denen du dich im Job beschäftigst
Job-Standorte
Das ist dein Arbeitgeber
SICK ist einer der führenden Produzenten von Sensoren und Sensorlösungen für die industrielle Automatisierung.
- 250+ Mitarbeitende
- Etablierte Firma
- Onsite, Hybrid, Full Remote
- Industrie, Produktion
Arbeitgeber-Reviews
- Karriere
- 3,6
- Arbeitsbedingungen
- 4,4
- Teamkultur
- 3,8
- Engineering
- 2,8
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