FY27 PhD Internship: Data-Centric AI for End-to-End Autonomous Driving - 6 months, Munich, Unterschleißheim, Germany
- Onsite
About the role
Company:
Qualcomm Technologies GmbHJob Area:
Interns Group, Interns Group > Workstudy InternGeneral Summary:
PhD Internship: Data-Centric AI for End-to-End Autonomous Driving
Location: Munich / Unterschleißheim, Germany
Duration: 6 months
Area: Autonomous Driving, End-to-End AI, Vision-Language Models, Data-Centric AI
We are looking for a highly motivated PhD student to join our AI Systems team in Munich and conduct research on automated data curation for end-to-end autonomous driving systems.
Modern end-to-end driving models depend heavily on the quality, diversity, and relevance of their training data. Large-scale driving datasets, however, contain substantial redundancy while rare and safety-critical situations remain difficult to identify systematically.
The goal of this internship is to develop data-centric AI techniques that automatically identify model weaknesses and translate observed real world driving failures into improved training datasets. The work will move beyond general data selection towards fine-grained, model-aware data curation using trajectory and action information, multimodal representations, and potentially Vision-Language Models (VLMs).
Your Profile
Minimum Qualifications
- Currently enrolled in a PhD program in Computer Science, Robotics, Artificial Intelligence, Machine Learning, Computer Vision, or a related technical field.
- Strong knowledge of machine learning and deep learning.
- Experience with PyTorch and Python.
- Experience in at least one of:
- Computer vision
- Autonomous driving
- Multimodal/Vision-Language Models
- Robotics
- Ability to develop and evaluate research ideas independently.
- Strong written and verbal communication skills in English.
Preferred Qualifications
- Research experience in end-to-end autonomous driving, behavior prediction, trajectory prediction, or robotics.
- Experience with VLMs/VLM evaluation, foundation models, or multimodal representation learning.
- Experience working with large-scale datasets and data-selection or active-learning techniques.
- Familiarity with closed-loop simulation or autonomous-driving evaluation.
- Publications or demonstrated research experience at major machine-learning, computer-vision, or robotics venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoRL, or ICRA.
What You Will Gain
This internship offers the opportunity to conduct applied research at the intersection of autonomous driving, multimodal foundation models, and data-centric AI.
The research has the potential to contribute directly to scalable data-flywheel systems in production for autonomous driving, connecting vehicle evaluation, failure discovery, intelligent data selection, and model retraining.
*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
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