PhD Position Foundation Models for Automotive Imaging Radar

Job description

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.

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

Faculty Mechanical Engineering 

From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, 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. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation. 

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

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.  

Additional information

For more information about this vacancy, please contact Prof. Dr. D.M. Gavrila at recruitment-me@tudelft.nl.

Application procedure

Are you interested in this vacancy? Please apply no later than 18 October 2026 via the application button and upload the following documents:

•  A motivation letter
• A detailed CV
• BS and MS transcripts (i.e. courses and grades)
• Any supporting materials e.g. link to MS Thesis (draft), publications, project descriptions, code repositories
• If the applicants are selected for an interview, they will be asked to provide contact details of at least two references with their consent

You can address your application to Prof. Dr. D.M. Gavrila.

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. For more details please check the Graduate Schools Admission Requirements. 

Please note:

• You can apply online. We will not process applications sent by email and/or post. 
• As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
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