The signal-processing group has a strong background in model-based signal processing using, among other things, tools from mathematical statistics and numerical methods. Our “classical” applications are found in wireless communications and radar signal processing, where we work with channel estimation, adaptive antennas, target tracking and high-resolution methods.
The mixture of basic research in statistical signal processing, machine learning and new application areas has been fruitful and we plan to continue in the same spirit. One of these resonably new application areas is automotive intelligent transportation systems (ITS) where we want to expand our presence with a postdoctoral researcher.
Description
Together with several other groups at Chalmers we are currently developing our own cooperative automated vehicle. Our part of the problem is to position the vehicle and to perceive the surrounding traffic situation using noisy sensor observations from, e.g., radar, lidar, camera and GPS. We do this by developing statistical models of the sensor observations and of the dynamics of the objects of interest, models that we can use to develop new sensor fusion methods around. The goal is to accurately position the host vehicle and describe the current traffic scene such that, e.g., the vehicle itself can autonomously navigate to a given destination.
Major responsibilities
The main focus of the research should be on the perception subsystem of cooperative automated vehicles. The goal is to support the development of an automated vehicle research platform in the form of an actual vehicle equipped with sensors and processing units.
The developed perception subsystem should solve the following problems,
• Positioning of the host vehicle (globally, relative to a map and relative to other road users),
• Models to represent the current traffic situation around the host vehicle (roads, infrastructure, vehicles, VRUs, etc.),
• Fusion algorithms to estimate the parameters in the aforementioned models using noisy sensor observations as well as vehicle-to-vehicle communication.
Your major responsibility as post-doc is to perform your own research in a research group. The position also includes teaching on undergraduate and master's levels as well as supervising master's and/or PhD students to a certain extent. Another important aspect involves collaboration within academia and with society at large. The position is meritorious for future research duties within academia as well as industry/the public sector. The research will be conducted in a team consisting of PhD students and senior researchers.
Position summary
The appointment is a full-time temporary employment (not a scholarship) for a period of not more than two years (1+1) and is co-funded by the Chalmers Area of Advance in Transportation.
Qualifications
To qualify for the position of post-doc, you must have a doctoral degree with experience from Bayesian filtering and estimation, statistical modeling and/or Simultaneous Localization and Mapping (SLAM). The degree should generally not be older than three years. Excellent verbal and written communication skills in English are expected. It is regarded as meritorious if you have experience in sensor fusion for vehicle positioning, in real-time implementation of sensor-fusion and filtering algorithms and of supervision and teaching.
For further information and how to apply, please visit:
www.chalmers.se/en/about-chalmers/vacancies/
Nr of positions available : 1
Research Fields
Technology
Career Stage
Experienced researcher or 4-10 yrs (Post-Doc)
Research Profiles
Recognised Researcher (R2)