PhD Student Position – Deep Learning from Unlabelled Sensor Data in Large Urban Environments

TU Delft - IV Group / 3DUU Lab

PhD
TU Delft
Posted 4 months ago

See Vacany TUD00272 on TU Delft website.

Challenge: Learning to detect and segment 3D structures in urban environments without labelling large amounts of sensor data.
Change: Self-supervised learning on 3D and image sensor data from complementary data sources, such as ground-level vehicle sensors plus aerial imagery and lidar data.
Impact: Drastically reduce human annotation effort to map new environments or train new object detectors for autonomous driving.

Job description

In this project, you will develop state-of-the-art deep learning techniques to exploit camera and lidar sensor data with minimal human data annotation effort. Learning from unannotated sensor data facilitates integrating new sensors in intelligent vehicles, and efficiently mapping and adapting to new urban regions. To deal with a lack of annotations, we pursue self-supervised and weakly-supervised techniques that can exploit sensor data from complementary viewpoints, e.g. from ground-level vehicle sensors with aerial imagery from geoinformatics sources. Applications include reconstructing static 3D urban structures, and segmenting the dynamic objects.

The host research group – the Intelligent Vehicles group – focuses on environment perception, dynamics & control, and interaction with humans for intelligent vehicles and automated driving in complex unstructured urban environments. The group is part of the Cognitive Robotics department at the 3ME Faculty, which aims to develop intelligent robots and vehicles that will advance mobility, productivity and quality of life. The department combines fundamental research with work on physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators and haptic interfaces.

The new AI lab on 3D Understanding (3DUU) initiated by the 3D Geoinformation Research Group and the Intelligent Vehicles Group is one of the Delft Artificial Intelligence Labs (DAI-Labs). It is a cross-disciplinary research lab seeking to develop state-of-the-art AI techniques for interpreting 3D data and reconstructing 3D objects for large-scale urban applications.

Department
3DUU is a Delft Artificial Intelligence Lab (DAI-Lab). Artificial intelligence, data, and digitalization are becoming increasingly important when looking for answers to major scientific and societal challenges. In a DAI-lab, experts in ‘the fundamentals of AI technology’ along with experts in ‘AI challenges’ run a shared lab. As a PhD, you will work with at least two academic members of staff and three other PhD candidates. In total, TU Delft will establish 24 DAI-Labs where 48 Tenure Trackers and 96 PhD candidates will have the opportunity to push the boundaries of science by using AI. Each team is driven by research questions that arise from scientific and societal challenges and contribute to the development and execution of domain-specific education. Instead of the usual 4-year contract, you will receive a 5-year contract. Approximately a fifth of your time will be allocated to developing ground breaking learning materials and educating students in these new subjects. The experience you will gain by teaching will be invaluable for future career prospects. All team members have many opportunities for self-development. You will be a member of the thriving DAI-Lab community that fosters cross-fertilization between talents with different expertise and disciplines.

Requirements

  • Completed a MSc degree in computer science, artificial intelligence, applied mathematics, or robotics;
  • Strong interests and expertise in machine learning (in particular deep learning), numerical linear algebra, statistics, robotics and/or geoinformatics, preferably with a strong publication record;
  • An affinity with teaching and guiding students;
  • Excellent C++ and/or Python programming skills, and experience with Git;
  • Experience with deep-learning frameworks (PyTorch/TensorFlow) at the MSc level, preferably applied to point cloud data and 3D sensors (LiDAR, stereo vision) or 2D computer vision.
  • Ability to act independently as well as to collaborate effectively with members of a larger interdisciplinary team, take initiative, be result oriented, organized and creative.
  • Good command of verbal and written English.

Conditions of employment

TU Delft offers DAI-Lab PhD-candidates a 5-year contract (as opposed to the normal 4-years), with an official go/no go progress assessment after one year. Approximately a fifth of your time will be allocated to developing ground breaking learning materials and educating students in these new subjects.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3217 in the fifth year. 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 sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation. For generations, our engineers have proven to be entrepreneurial problem-solvers both in business and in a social context. TU Delft offers 16 Bachelor’s and 32 Master’s programmes to more than 23,000 students. Our scientific staff consists of 3,500 staff members and 2,800 PhD candidates. Together we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact!

Faculty Mechanical, Maritime and Materials Engineering

The Faculty of 3mE carries out pioneering research, leading to new fundamental insights and challenging applications in the field of mechanical engineering. From large-scale energy storage, medical instruments, control technology and robotics to smart materials, nanoscale structures and autonomous ships. The foundations and results of this research are reflected in outstanding, contemporary education, inspiring students and PhD candidates to become socially engaged and responsible engineers and scientists. The faculty of 3mE is a dynamic and innovative faculty with an international scope and high-tech lab facilities. Research and education focus on the design, manufacture, application and modification of products, materials, processes and mechanical devices, contributing to the development and growth of a sustainable society, as well as prosperity and welfare.

Click here to go to the website of the Faculty of Mechanical, Maritime and Materials Engineering.

Additional information

For information about this vacancy or the selection procedure, you can contact Julian Kooij, Assistant Professor, email: J.F.P.Kooij@tudelft.nl.

Application procedure

To apply, please prepare:

  • a letter of motivation describing your fit to the position,
  • 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),
  • the names and contact addresses of two references.

All these items should be combined in one PDF document. Please email your application to Hilma Bleeker, email: application-3mE@tudelft.nl by July 10th. When applying for this position, please refer to the vacancy number TUD00272.

A pre-employment screening can be part of the application procedure.

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