Vacancies

Professor position

Data Fusion for Intelligent Vehicles

Job description

The Cognitive Robotics department at TU Delft seeks to fill a faculty position in the area of data fusion for intelligent vehicles at the level of Assistant (tenure-track) or Associate Professor (tenured). The position covers multi-modal sensor processing (e.g. vision, radar, LiDAR, GNSS/INS) for on-board environment perception and localization/mapping, in complex and dynamic environments (e.g. urban traffic). Specific topics of interest include:

  • Object detection (position, pose and shape estimation)
  • Environment representation (e.g. sensor-level, grid-level, object-level)
  • Multi-target tracking
  • Simultaneous localization and mapping (SLAM)
  • Distributed sensing
  • Life-long mapping of large scale environments
  • Statistical methods and machine learning incl. deep learning
  • Heterogeneous and temporal data fusion.


Requirements
Applicants should have the following qualifications:

  • PhD degree in Computer Science, Artificial Intelligence, Electrical/Mechanical Engineering, or related discipline. Experience in robotics and/or intelligent vehicles is an asset
  • Experience as a Post-Doc/Assistant Professor
  • Excellent track record in scientific research, as evident from publications in top-tier conferences and journals
  • Proven ability to provide inspiring teaching at both undergraduate and graduate levels (in English)
  • High motivation to pursue and establish an own research direction within an interdisciplinary environment
  • Organizational and managerial skills to interact and cooperate effectively with staff and other research institutes and organizations, including industry  
  • Experience in the acquisition of external funding.


Conditions of employment
A tenure track position is offered for a maximum of six years. Tenure Track is a process leading up to a permanent appointment with the prospect of becoming an Associate Professor. During the Tenure Track, you will have the opportunity to develop into an internationally acknowledged and recognized academic. To support that, we offer a structured career and personal development program, which accounts for individual needs and preferences. For more information about the tenure track and the personal development program, please visit www.tudelft.nl/tenuretrack.

Based on performance indicators agreed upon at the start of the appointment, a decision will be made at the end of the fifth year whether to offer you a permanent faculty position.

The salary for a Tenure Track (Assistant Professor) position is min. €3.545 to max. €5.513 per month gross. For exceptionally strong candidates, a shortened tenure track period or Associate Professor position can be considered.
For an Associate Professor position different terms of employment apply. Depending on background and experience, the salary can range from min. €4.911 to max. €6.567 per month gross. All salaries mentioned are based on full time contracts.

The TU Delft offers a customizable 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. The TU Delft offers trainings to improve English and Dutch language competencies.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

TU Delft creates equal opportunities and encourages women to apply.

Employment: Temporary, Tenure track, permanent

Employer
Delft University of Technology
Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

Faculty & Department
Faculty Mechanical, Maritime and Materials Engineering
The 3mE Faculty trains committed engineering students, PhD candidates and post-doctoral researchers in ground breaking scientific research in the fields of mechanical, maritime and materials engineering. 3mE is the epitome of a dynamic, innovative faculty, with a European scope that contributes demonstrable economic and social benefits. 

The Cognitive Robotics department has the mission 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. Of special interest are robotic solutions for complex, human-inhabited environments. Collaborations exist with cross-faculty institutes (TU Delft Robotics Institute and TU Delft Transport Institute), the national robotic ecosystem (RoboValley, Holland Robotics) and international academia and industry. 

The faculty opening is within the Intelligent Vehicles group; it focuses on methods for environment perception, dynamics and control and human factors in the context of automated driving.
For more information, see Cognitive Robotics (CoR) and Intelligent Vehicles.

Information and application
For more information about this position, please contact  Prof. D.M. Gavrila (e-mail: d.m.gavrila@tudelft.nl).

To apply, please submit (in one single pdf file):

  • a motivation letter,
  • a detailed CV,
  • a research and teaching statement,
  • electronic copies of your top three publications, and
  • contact data of three references. 

Applications should be submitted to Application-3mE@tudelft.nl, referring to vacancy number 3mE19-57 in the subject of the email. The vacancy closes as soon as we have found a talented candidate for this position

Postdoc position

Deep-Learning Based Radar Processing for Intelligent Vehicles

JOB DESCRIPTION

The Intelligent Vehicles group at the TU Delft, the Netherlands, invites applications for a fully funded Post-Doctoral research position in the area of Deep-Learning Based Radar Processing in Intelligent Vehicles. The intended research addresses problems within the spectrum of object detection, semantic scene analysis and vehicle localization.  Apart from radar-based processing, data fusion with video is of interest. The position is funded by industry partner NXP.

       REQUIREMENTS

We are seeking Post-Doc applicants with an interest in performing cutting edge research in an active and exciting research area (cf. self-driving cars by Google, Apple and the automotive industry). Prospective applicants should have a strong academic record with a solid background in sensor processing (vision/radar/LiDAR, sensor fusion) and Machine Learning (in particular: Deep Learning). Good programming skills are expected, preferably in C++/Python. Knowledge of deep-learning frameworks (TensorFlow/PyTorch/Keras/Caffe) and OpenCV/ROS/CUDA is a plus. A certain affinity towards turning complex concepts into real-world practice (i.e. vehicle demonstrator) is desired. The successful candidate is expected to be able to act independently as well as to collaborate effectively with members of a larger team. Good English skills are required.

       CONDITIONS OF EMPLOYMENT

Fixed-term contract: 24 months.

 

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.
Living conditions in the Netherlands (e.g. Delft, Hague, Amsterdam) are considered to be among the very best in Europe. The TU Delft scores consistently high in international comparisons (e.g. within top 20 in QS World Univ. Rankings in Engineering and Technology).

       EMPLOYER

Technische Universiteit Delft

Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

       DEPARTMENT

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.

For more information about the Intelligent Vehicles group at TU Delft, see http://intelligent-vehicles.org.

       ADDITIONAL INFORMATION

For additional information regarding this vacancy, please contact Prof. Dariu Gavrila, head Intelligent Vehicles group, see http://intelligent-vehicles.org.

Applications should include a motivation letter explaining why you are the right candidate, a CV, a transcript of graduate-level courses (M.S., Ph.D.), a link to your Ph.D. Thesis, a list of projects you have worked on with brief descriptions of your contributions (max 2 pages), a list of your publications and the names and contact addresses of two references. All these items should be combined in one PDF document. Please submit this document at the earliest convenience to Dariu Gavrila, Head Intelligent Vehicles group, application-3mE@tudelft.nl. When applying for this position, please refer to vacancy number 3mE20-49.

PhD position

Control of Teams of Autonomous Drones in Hazardous Environments (two positions)
Acoustic Detection of Occluded Urban Traffic for Intelligent Vehicles
Deep Learning from Unlabelled Sensor Data in Large Urban Environments
Learning of socially compliant motion planning for autonomous vehicles

       Job description

We are looking for an ambitious PhD candidate who would like to develop novel methods for safe and socially compliant autonomous navigation in crowded urban canals, with a combination of machine learning (learning from historical data, reinforcement learning) and trajectory optimization approaches.

You will join a team of researchers within the context of the project "Sustainable Transportation and Logistics over Water: Electrification, Automation and Optimization (TRiLOGy)" funded by the Dutch Research Council (NWO). In this project, we will investigate (i) fleet management decisions at the high level (1 PhD position supervised by Assist. Prof. B. Atasoy) and (ii) autonomous navigation methodologies for autonomous vessels in urban canals (1 PhD position supervised by Assist. Prof. J. Alonso-Mora). You will be responsible for the latter, autonomous navigation.

The objective of the autonomous navigation part is to develop autonomy tools for navigation in inland waterways, among other manned and unmanned vessels. The main challenge to ensure safe and efficient navigation of autonomous vessels in urban waters is that of generating safe trajectories that (i) take into account the complex dynamics of the vessel, (ii) coordinate with other traffic participants and (iii) show socially-compliant behavior based on past experiece and historical data. In TRiLOGy we will rely on historical data from manned vessels and machine learning strategies (supervised learning, reinforcement learning, multi-agent reinforcement learning) to improve the performance of the motion planning system (trajectory optimization) and produce feasible human-like motions for the autonomous vessel. The developed motion planners will closely interact with the perception modules of the autonomous vessel. A typical scenario is that of crowded canals and intersections, where efficient navigation can be achieved with tight coordination among the interacting participants.

The autonomous navigation methods that will be developed in this project will be tested and verified through their application to autonomous vessels in the ResearchLab Autonomous Shipping (RAS). You will also interact with our industrial partners (Zoev City, Municipality of Amsterdam, Flying Fish and DEMCON Unmanned Systems), with the Amsterdam Institute of Advanced Metropolitan Solutions (AMS) and with MIT researchers working on the AMS Roboat project.

The PhD candidate will be embedded within the Autonomous Multi-robots Lab of the Department Cognitive Robocs at TU Delft. For more information of our ongoing research see https://www.autonomousrobots.nl.

       Requirements

The candidate has a very good MSc degree in Robotics, Computer Science, Systems and Control, Electrical/Mechanical Engineering, Applied Mathematics, or a related field. The candidate must have strong analytical skills and must be able to work at the intersection of several research domains. Good programming skills and experience with Python/C++ and ROS are of foremost importance to implement the learning methods and the proposed designs on real ASVs. A very good command of the English language is required, as well as excellent communication skills. Candidates having exhibited their ability to perform research in machine learning, control, optimization, perception and/or robotics are especially encouraged to apply.

       Conditions of employment

Fixed-term contract: 4 years.

 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.

       Employer

Technische Universiteit Delft

Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

       Department

Faculty Mechanical, Maritime and Materials Engineering

The 3mE Faculty trains committed engineering students, PhD candidates and post-doctoral researchers in groundbreaking scientific research in the fields of mechanical, maritime and materials engineering. 3mE is the epitome of a dynamic, innovative faculty, with a European scope that contributes demonstrable economic and social benefits.

The main focus of the Cognitive Robotics department is the development of intelligent robots and vehicles that will advance mobility, productivity and quality of life. Our mission is to bring robotic solutions to human-inhabited environments, focusing on research in the areas of machine perception, motion planning and control, machine learning, automatic control and physical interaction of intelligent machines with humans. We combine fundamental research with work on physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators and haptic interfaces. Strong collaborations exist with cross-faculty institutes TU Delft Robotics Institute and TU Delft Transport Institute), our national robotic ecosystem (RoboValley, Holland Robotics) and international industry and academia.

       Additional information

If you have specific questions about this position, please contact Assist. Prof. J. Alonso-Mora (j.alonsomora@tudelft.nl, +31 152785489)  Always specify the vacancy number in the email subject. Please do not send application emails to these email addresses but use the specified address (application-3mE@tudelft.nl).

To apply, please send via e-mail:
• a letter of motivation explaining why you are the right candidate for this project,
• a detailed CV,
• a complete record of Bachelor and Master courses (including grades),
• your Master Thesis (at least as draft),
• 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 or three references.

All these items should be combined in one PDF document. Applications should be submitted at the earliest convenience to application-3mE@tudelft.nl. When applying for this position, always refer to the vacancy number 3mE20-44. The review of applications will continue until the position is filled. The intended starting date is fall 2020 (flexible).