PhD - Data-driven Self-Assessment for Multimodal Perception in Automated Driving
Aufgaben
Data-driven methods are ubiquitous in today's autonomous systems. An important task of environmental perception is the detection, classification, and tracking of relevant objects in the scene. We are particularly interested in environment perception using point cloud like data (e.g. lidar) in combination with video.
Today's perception algorithms are based on deep neural networks and usually are trained to equally weight errors for each object in a scene, independent of its potential effects on the driving task. However, in reality there are objects which are more and less relevant. As a result, the trained network which is considered to perform best according to the metrics, not necessarily is the best one to be deployed. The goal of this research is to 1) develop new ways of assessing the relevance for all parts of the scene, 2) and assessing the current perception performance within these regions. In particular connecting the concepts of relevance and self-assessment to improve the correlation of training metrics and real-world performance will be the main focus.
- You develop novel machine learning approaches for object detection and tracking based on self-supervision techniques.
- Furthermore, you evaluate your algorithms on public benchmark data sets and internal real-world data sets - offline and online.
- You contribute to the scientific community with publications on top machine learning and robotics conferences and journals (NIPS, ICML, ICLR, CVPR, ICCV, IROS or ICRA).
- Take on responsibility and work in an agile and diverse research team with other PhD students and with exchange across several research projects.
Profil - Education: degree (Master/Diploma) in Computer Science, Electrical Engineering, Mathematics or related field with excellent academic achievements
- Experience and Knowledge: profound knowledge of machine learning algorithms and principles, preferably deep learning and proven programming skills in Python
- Personality and Working Practice: open-minded team player who is goal-oriented and logical thinking
- Languages: fluent in English (written and spoken), German is a plus
- Education: degree (Master/Diploma) in Computer Science, Electrical Engineering, Mathematics or related field with excellent academic achievements
- Experience and Knowledge: profound knowledge of machine learning algorithms and principles, preferably deep learning and proven programming skills in Python
- Personality and Working Practice: open-minded team player who is goal-oriented and logical thinking
- Languages: fluent in English (written and spoken), German is a plus
Kontakt & Wissenswertes
https://www.bosch-ai.com
www.bosch.com/research
The PhD project will be carried out in cooperation with and under co-supervision of Dr. Holger Caesar (Assistant Professor at the Intelligent Vehicles Lab, TU Delft).
Start: according to prior agreement
Please submit all relevant documents (incl. curriculum vitae, motivation letter, and certificates).
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223
Need further information about the job?
Florian Faion (Functional Department)
+49 711 811 33853
Einblicke in unsere Arbeitswelt
Willkommen in Renningen
Unser Forschungscampus in Renningen bildet den internationalen Knotenpunkt unserer Bereiche Forschung und Vorausentwicklung, Cross-Domain Computing Solutions und Bosch Center for Artificial Intelligence. Mitarbeiter aus aller Welt arbeiten daran, Antworten auf die Fragen von Übermorgen zu finden. Damit sich die Ideen unserer Forscher optimal entfalten können, ist der Campus ein Netz der kurzen Wege zwischen Kommunikation und Inspiration, an dem der Kreativität keine Grenzen gesetzt sind. Wollen auch Sie die Zukunft gestalten? Wir freuen uns auf Ihre Neugier und Innovationsfreude.
