1 dag geleden - Radboud Universiteit - Nijmegen
Als promovendus gaat u werken bij onderstaand onderzoek. In samenwerking met Advanced Bionics European Research Center in Hannover, een toonaangevende producen…
Sensor fusion, localisation and mapping, automated dynamic modelling, robotic motion control, physical human-robot interaction
Candidates are expected to be, or be on their way to becoming, authorities in their own field of research, and to complement and enrich the existing research programmes in the department. For the overall balance in the department, we especially welcome applications from female scientists. We expect you to:
• Develop, conduct, and supervise high-quality academic research in your field;
• Teach general and specialised courses at undergraduate and post-graduate levels, and inspire students through teaching and post-graduate supervision;
• Interact and collaborate with other researchers and specialists in academia and industry within multidisciplinary research projects;
• Bridge the gap between theoretical knowledge and the development of new, innovative technologies;
• Establish and execute externally funded research programmes;
• Be an inspiring contribution to our staff.
A Tenure Track, a process leading up to a permanent appointment with the prospect of becoming an Associate or full Professor, offers young, talented academics a clear and attractive career path. During the Tenure Track, you will have the opportunity to develop into an internationally acknowledged and recognised academic. We offer a structured career and personal development programme designed to offer individual academics as much support as possible. For more information about the Tenure Track and the personal development programme, please visit www.tudelft.nl/tenuretrack.
Applicants should have the following qualifications:
• PhD degree in Computer Science, Mechanical/Electrical Engineering, Applied Mathematics, Systems and Control, Artificial Intelligence or a related discipline.
• Experience in robotics and/or intelligent vehicle technology is an asset.
• Strong analytical skills and ability/desire to work at the intersection of several research domains.
• At least several years of experience as a post-doc or Assistant Professor, and experience outside the Netherlands.
• Excellent track record in scientific research, as evident from papers in international and peer-reviewed journals.
• Proven ability to deal with scientific and engineering challenges in close interaction with industrial partners.
• Proven ability to provide inspiring teaching at both undergraduate and post-graduate levels (in English).
• Organisational and managerial skills to interact and cooperate effectively with staff and other research institutes and organisations.
• Experience in acquisition of external funding.
• High motivation for implementing your own vision within the interdisciplinary environment that CoR offers.
• Team player mentality with the ability to communicate clearly, and eagerness to look for and exploit synergies that CoR and its network provides.
Delft University of Technology (the 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. The TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At the TU Delft you will work in an environment where technical sciences and society converge. The TU Delft comprises eight faculties, unique laboratories, research institutes and schools.
Mechanical, Maritime and Materials Engineering
To further strengthen robotic research and education, several key groups have now joined forces to form a new Department of Cognitive Robotics, with the mission of bringing robotic solutions to human-inhabited environments. We combine fundamental research with work with physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators, and haptic interfaces. Strong collaborations exist within cross-faculty institutes (TU Delft Robotics Institute and TU Delft Transport Institute), our national robotic ecosystem (RoboValley, Holland Robotics), international industry and academia. The strategic decision to form the Cognitive Robotics department, financially supported by the Executive Board of the Delft University of Technology, allows us to hire new academic staff for several Assistant Professor (tenure-track) and Associate Professor (tenured) positions. We are looking for excellent candidates with a proven track record in scientific research, an innovative research vision, and a commitment to education.
The faculty positions are in the following areas complementing our current research at the department:
Research focus on grid-level and object-level fusion, multi-target tracking, extended object tracking and deep learning for multi-modal and temporal data fusion (video, lidar and radar) – applicable to the intelligent vehicles domain.
Localisation and Mapping:
Research focus on map generation from heterogeneous and distributed sensors (ground- and aerial-based), life-long mapping of large-scale complex and dynamic environments, and accurate localisation in GPS denied areas - applicable to intelligent vehicles and other robotics domains.
Automated dynamic modelling:
Research focus on novel learning algorithms along with rich sensor data for the automated creation of highly predictive internal dynamic models, applicable to all types of robots yet initially focused on self-driving vehicles.
Robot motion control:
Research focus on nonlinear methods for robot motion control, including robust state estimation and sensor data fusion, applicable to dexterous manipulation and grasping.
Physical human-robot interaction:
Research focus on intuitive interaction and mutual learning between humans and (mobile) robot manipulators that operate in unstructured environments; based on insights from human-centred design, bilateral telemanipulation, (haptic) shared control, learning from demonstration, and human-robot teamwork.
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