PhD Position Foundation Models for Automotive Imaging Radar

Delft University of Technology; Delft; Vor Ort; EUR 36.000 - 45.000; Vollzeit

Zusammenfassung

Delft University of Technology invites applications for a PhD project investigating foundation models for automotive imaging radar. You will work within the Intelligent Vehicles group in the Department of Cognitive Robotics, collaborating with Perciv.AI and NXP Semiconductors, using real sensor data from research vehicles.

The position is a four-year employment contract with competitive Dutch university standards, salary €3,204–€4,051 per month, plus holiday allowance and a year-end bonus.

Qualifikationen

• MSc degree in Computer Science, AI, Robotics, Electrical Engineering, or closely related field.
• Strong background in machine learning and deep learning.
• Ability to develop, understand, and critically evaluate ML research software, preferably using Python and PyTorch.
• Interest in foundation models, self-supervised learning, multimodal learning, and 3D perception.
• Affinity for translating methodological research into experiments with real automotive sensor data.
• Ability to work independently as well as in a team.
• Good written and spoken English.
• Experience with radar, signal processing, computer vision, autonomous driving, or sensor fusion is advantageous.

Kenntnisse

Machine learning

Deep learning

Python

PyTorch

Foundation models

Self-supervised learning

Multimodal learning

3D perception

Radar knowledge

English proficiency

Ausbildung

MSc in Computer Science/AI/Robotics/Electrical Engineering or related field

Tools

None

Jobbeschreibung

Job description

In this PhD project, you will investigate foundation models for automotive imaging radar.

Next generation automotive imaging radars provide increasingly rich 3D information, opening the door to more advanced scene understanding and perception tasks such as 3D object detection and free space estimation. However, current neural networks are typically developed for a specific radar configuration and task, and often generalize poorly to other sensors. At the same time, large scale radar datasets with high quality labels remain scarce.

The goal is to learn general radar representations from largely unlabelled data that can generalize across different radar configurations and be efficiently adapted to multiple downstream perception tasks with limited labelled data. An important research direction is the transfer of representations from vision and LiDAR based foundation models to radar, as well as the development of multimodal foundation models incorporating radar. A further challenge is how different radar representations and sensor configurations can be accommodated within a general foundation model framework, and to what extent a common model can generalize across them. Research directions may also include generative or predictive modeling of dynamic radar scenes.

The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access to research vehicles and advanced radar prototypes for collecting and evaluating real world multimodal sensor data.

While the primary application domain is automotive, the developed methods should preferably be sufficiently general to also apply to other domains, such as autonomous vehicles operating in off-road settings.

You will join the Intelligent Vehicles group within the Department of Cognitive Robotics at TU Delft. The project will be carried out in collaboration with Perciv.AI, and will also involve NXP Semiconductors as an industrial partner. The research is part of the FIND project, funded by the Dutch Research Council (NWO), which investigates foundation models for high tech industry applications.

Your results are expected to be published at leading international venues in machine learning, computer vision, robotics and radar, such as NeurIPS, CVPR, ICRA, IEEE IV and RadarConf. For your research, you will have access to extensive computing resources at TU Delft, ranging from personal workstations and shared GPU servers to the Delft AI Cluster and the DelftBlue supercomputer. Your supervisors will be Prof. Dariu Gavrila and Dr. Julian Kooij.

Job requirements

We are looking for a candidate with:

• 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.

TU Delft (Delft University of Technology)

Working at TU Delft means contributing to solutions that really make a difference.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional.

Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty Mechanical Engineering

Research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3204 - €4051 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible. The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis.

As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. The assessment takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed.