PhD: Foundation Models for Automotive Imaging Radar

Delft University of Technology (TU Delft); Netherlands; On-site; EUR 36,000 - 45,000; Full time

Job summary

Delft University of Technology (TU Delft) invites applications for a PhD candidate in the Intelligent Vehicles group, Department of Cognitive Robotics. You will investigate foundation models for automotive imaging radar, learning general radar representations from largely unlabelled data to generalize across configurations and tasks.

The project combines methodological ML research with real automotive sensor data, with access to research vehicles and prototypes, and collaboration with Perciv.AI

Qualifications

• An MSc degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a closely related field.
• A strong academic record and solid background in machine learning and deep learning.
• Ability to develop, understand, and critically evaluate machine learning research software, preferably using Python and PyTorch.
• An interest in foundation models, self supervised learning, multimodal learning, and 3D perception.
• An affinity for translating methodological research into experiments with real automotive sensor data and vehicle demonstrators.
• The ability to work independently as well as collaborate effectively within a larger research team.
• Good written and spoken English.
• Experience with radar, signal processing, computer vision, autonomous driving, or sensor fusion is advantageous.

Skills

Machine learning

Deep learning

Python

PyTorch

Foundation models

Self-supervised learning

Multimodal learning

3D perception

Research software

Independent work

Education

MSc in Computer Science, AI, Robotics, Electrical Eng or related

Job description

Delft University of Technology (TU Delft) invites applications for a PhD candidate in the Intelligent Vehicles group, Department of Cognitive Robotics. You will investigate foundation models for automotive imaging radar, learning general radar representations from largely unlabelled data to generalize across configurations and tasks.

The project combines methodological ML research with real automotive sensor data, with access to research vehicles and prototypes, and collaboration with Perciv.AI