Wissenschaftliche/r Mitarbeiter/in im Bereich "Perception, decision making and control of autonomous systems in robotics" (m/w/d)
About us
The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.
https://www.mv.uni-kl.de/mec/home.
Research Framework
Research on autonomous systems in robotics combines traditional and modern control techniques with artificial intelligence (AI) by considering a holistic system characterised by sensor-based perception, AI-driven decision making, followed be the actuator-based physical interaction with the environment. This multifaceted research opportunity involves conducting in-depth investigations, designing experiments, and developing algorithms to improve the capabilities of autonomous systems. The tasks encompass challenges in different domains, such as environment perception, scene understanding, sensor fusion, modeling, data analysis and simulation. Researchers in perception and decision-making for autonomous systems play a critical role in developing safe and efficient algorithms, tighty coupled with control algorithms to finally ensure safe and accurate task and action execution.
Task Description
Research activities in this field involve a combination of theoretical work, software and hardware development, algorithm implementation, testing, and evaluation.
- Review the literature in the field of perception, scene understanding and decision making for autonomous systems,
- Literature review in the related fields followed by trainining and evaluation of deep/machine learning models like semantic scene segmentation and object detection
- Develop and implement deep/machine learning models for scene understanding, object detection, tracking
- Model the detected static and dynamic objects in an interactive graph and build graph neural networks for scene understanding
- Representing the interactive graph as a spatial-temporal graph of the environment that can be used for a safe path planning and decision-making
- Working with different sensors like LiDAR, cameras, and radar.
- Explore and experiment with mRulti-task learning techniques to simultaneously handle various tasks, such as lane detection, object recognition, and traffic sign identification
- Integrate and test the developed algorithms in real time on test vehicle
Qualification
- Above average university degree in computer engineering, computer science, or mathematics
- Advanced computer vision skills
- Practical experience with convolutional neural networks, graph neural networks and deep learning libraries e.g. Pytorch, Tensorflow
- Excellent programming skills in Python or C++
- Proven research experience in the field of computer vision
- Organizational and cooperation skills with scientific as well as industrial partners of different disciplines
- Proficiency in English or / and German is essential
We offer
- Payment according to TV-L E13 with an initial one-year time limit
- The possibility to do a PhD and to teach is given in case of scientific aptitude
- TUK strongly encourages qualified female academics to apply
- Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
- Electronic application is preferred. Please attach only one coherent PDF.
You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.
Contact
Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax: +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de
Keywords
Artificial Intelligence
Perception
Autonomous Systems
Application Papers
Cover Letter
CV
University Certificates
References
List of Publications
Application Deadline
15. April 2024
We will process your application as soon as received.
Job Availability
Immediate