> Markdown version of [/jobs/ext/4017637-thesis-learning-task-relevant-latent-spaces-for-sim-to-real-transfer](https://www.wearedevelopers.com/jobs/ext/4017637-thesis-learning-task-relevant-latent-spaces-for-sim-to-real-transfer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Thesis: Learning Task-Relevant Latent Spaces for Sim-to-Real Transfer - **Company:** SICK AG - **Location:** Waldkirch, Germany - **Contract:** Temporary contract - **Skills:** Google AdWords, Artificial Intelligence, Artificial Neural Networks, Computer Vision, Google Analytics, Human-Computer Interaction, Python (Programming Language), Machine Learning, Object Detection, Web Analytics, Web Traffics, Google Tag Manager, Pytorch, Transfer Learning, Deep Learning, Information Technology, Feature Extraction - **Published:** October 30, 2026 - **Apply:** https://www.careerjet.de/jobad/de544332a70945d44f1065e2aba63c3c5d ## About the Role * Currently pursuing an advanced master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Robotics, Physics, Engineering, or a related field * Good knowledge of machine learning and deep learning, with practical experience in computer vision and neural networks * Knowledge of representation learning, self-supervised learning, or synthetic data is an advantage * Good Python programming skills and practical experience with PyTorch or a comparable deep learning framework * 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 You don't meet every requirement yet? We still look forward to receiving your application. We value curiosity, a willingness to learn, and the ability to approach complex questions systematically. You will have the opportunity to develop any missing skills as part of your thesis. ## Description Thesis: Learning Task-Relevant Latent Spaces for Sim-to-Real Transfer Summer Semester 2027 - fixed term of 3-6 months 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. YOUR TASKS: * 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., We use Google Tag Manager, a service provided by Google Ireland Limited (Gordon House, Barrow Street, Dublin 4, Ireland). Google Tag Manager is used solely to manage and trigger website tags via a user interface. Google Tag Manager itself does not set cookies and does not process any personal data. Processing only takes place through the individual tags that are implemented via the Tag Manager (e.g. Google Analytics 4, Google Ads). These tags are activated exclusively on the basis of your consent via our cookie/consent management platform (Art. 6(1)(a) GDPR). If you do not give consent for statistical or marketing tools, only technically necessary tags will be triggered. Controller: Google Ireland Limited, Gordon House, 4 Barrow Street, Dublin, D04 E5W5, Ireland Contact details of Google's Data Protection Officer: https://support.google.com/policies/contact/general_privacy_form Further information: * Privacy Policy: https://business.safety.google/privacy/ * Cookie Policy: https://policies.google.com/technologies/cookies International data transfers: As part of Google services, data may be transferred to third countries (e.g. the United States). Google relies on the EU Standard Contractual Clauses for such transfers. Consent to cookies from provider GoogleTagManager Advertising Cookies These cookies serve ads that are relevant to your interests. You may freely choose to accept or decline these cookies at any time. Note that certain functionality that these third parties make available may be impacted if you do not accept these cookies. Consent to all Advertising Cookies Show More Details Advertising Cookies Provider Description Enabled Google Analytics Google Analytics is a web analytics service offered by Google that tracks and reports website traffic.