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  3. PhD: Tensor decomposition for Efficient Robotic Perception

PhD: Tensor decomposition for Efficient Robotic Perception

The Intelligent Vehicles group and the Delft Center for Systems and Control (DCSC) at the TU Delft is seeking a PhD candidate with an interest in performing …

ongeveer 2 maanden geleden


Mekelweg, Delft, Zuid-Holland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
38 - 38 uur
€ 2325 - € 2972 per maand


The Intelligent Vehicles group and the Delft Center for Systems and Control (DCSC) at the TU Delft is seeking a PhD candidate with an interest in performing fundamental research on Tensor Decomposition and Deep Learning in the exciting research area of self-driving vehicles.

Mobile robotics and intelligent vehicles with many multi-modal sensors collect and process large amounts of data during operation using cameras, lidar scanners, radars, and possibly even microphone arrays. Indeed, the current state-of-the-art in perception, modelling and control is mostly data-driven. However, the abundance of high bandwidth sensors data results in both online and offline processing bottlenecks. This flooding of data is a major technical limitation in the development of performant algorithms for modelling and control. For online processing, the data flood leads to large storage space requirements, high bandwidth needs for real-time communication, and computational challenges. For offline processing, more efficient methods are needed to train machine learning models on large amounts of mixed, unlabelled data.

This PhD position will explore the use of suitable Tensor Decompositions (TD) to efficiently represent the streaming sensor data. TD exploits low-rank properties of tensor data, similar to the SVD for matrices, which facilitates important real-time high-volume perception tasks found in robotics, and intelligent vehicles specifically. While the self-driving vehicle domain will be main application area in this proposal, developed methods will also benefit other robotics applications, and be of interest to the wider machine learning, applied mathematics, and control communities.


Prospective applicants should have a strong academic record in computer science, artificial intelligence or robotics with solid background in numerical linear algebra, statistics, machine learning, and/or robotics. Good software development and programming skills are expected, preferably in C++ and Python/Julia. Knowledge of deep-learning frameworks (Torch/TensorFlow) and ROS/CUDA is a plus. A certain affinity towards turning complex concepts into real-world practice (i.e. vehicle demonstrator) is desired. All applicant(s) are expected to be able to act independently as well as to collaborate effectively with members of a larger team. Good command of the English language is required (knowledge of Dutch is not a necessity).


TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.tudelft.nl/phd for more information.

Additional information

For information about this vacancy, you can contact Dr. Julian Kooij, email: J.F.P.Kooij@tudelft.nl and add to the cc field Dr. Kim Batselier, email: K.Batselier@tudelft.nl.

To apply, please submit to application-3mE@tudelft.nl by 1 July 2020:
• a letter of motivation explaining why you are the right candidate,
• a detailed CV,
• a complete record of Bachelor and Master courses (including grades),
• your Master’s Thesis,
• any publications, and a list of projects you have worked on with brief descriptions of your contributions (max 2 pages), and the names and contact addresses of two references.

All these items should be combined in one PDF document. When applying for this position, please refer to vacancy number 3ME18-02.


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