Validation of Systems with Machine Learning Components

Now we are looking for someone to join us as our next Volvo Industrial PhD (VIPP) candidate within the area Architectural Design and Verification / Validation of Systems with Machine Learning Components.

As a Industrial PhD candidate, you will have a close cooperation with the department of Electrical System Design, Electrical System Integration and the Machine Learning & Artificial Intelligence Center of Excellence.

At the departments of Electrical System Design and Electrical Integration and Development, we create value by continuously enabling an effective and quality assured software & electronics system. This includes the development of the electrical architecture and HW/SW platform for the cars on top of which applications can be developed, as well as tools, methods and processes for the system development and integration.

The Machine Learning (ML) & Artificial Intelligence (AI) Center of Excellence is responsible for setting up and driving the integration of Volvo Cars’ overall machine learning strategy. The department also handles competence growth and collaborations within the field of machine learning. Our team is cross-functional, consisting of Data Engineers, Data Scientists, Machine Learning Engineers and Software Engineers developing product applications and services with direct or indirect connection to our cars.

Background and main responsibilities
Machine Learning (ML) has shown large potential for solving problems intractable by classical methods. These include pattern recognition in unstructured data such as images, a key enabling technology for e.g., automated driving. It has also been shown to be a useful tool for other types of various data analysis tasks within the automotive domain.

Due to their stochastic nature, using ML components requires specific approaches related to the architectural design and verification/validation of the electrical system of the vehicle. Suitable architecture containing both stochastic and traditional deterministic components shall be defined which assures safety, robustness, fault tolerance, and the exchange of data between ML components and the rest of the vehicle. Furthermore, the nature of ML algorithms makes them difficult to test and analyse from a safety perspective, which requires implementation of specific verification and validation methods.

In order to address the above-mentioned needs, this project aims to:
•    Define and evaluate different architectural styles and patterns for developing systems with ML components and exchange of extracted information between their independent subsystems.
•    Develop methods for validation and verification, enabling use of ML for safety relevant vehicle systems with high accuracy and availability requirements.

In the short term, the project is expected to contribute to the development of safe, robust and fault tolerant electrical systems with ML components in vehicles. In the long run, the project is expected to contribute to the continuous deployment of ML components to different autonomous subsystems in cars, and their safe and reliable utilization in the transportation eco-system.

As part of the Volvo Industrial PhD Program (VIPP), the candidate is expected to conduct research in the area of architectural design and verification/validation of systems with machine learning components, in order to address the above defined objectives. In addition to the above mentioned departments, the candidate will also collaborate with other researchers within the VIPP program at Volvo and academia. The academic affiliation will be at the Software Engineering division at the department of Computer Science and Engineering at Chalmers | University of Gothenburg (UGOT).

Who you are
We are looking for a curious person that are hungry to constantly learn and develop. You have a positive attitude and enjoy being part of a team. We also believe that you enjoy communicating and dare to speak in front of large audiences.
Required for the role is:
•    M.Sc. within computer science, software electronics or equivalent.
•    Good programming skills and experience in working with machine learning algorithms.
•    Good understanding of machine learning theory and algorithms.
•    Good knowledge of architectural design patterns and strategies.
•    Good understanding of system verification techniques, testing and quality assurance.
•    Experience in the development of large systems is meritorious.

You should have a basic knowledge of different research methodologies. Scientific publications and some experience in the development of large systems will be considered as meritorious. We offer you an international environment and good knowledge in English, both spoken and written, is essential.

Application and contact
We apply a continuous selection process so we recommend that you submit your application in English as soon as possible, but no later than 2019-12-15. Note that we do not accept applications via e-mails. If you have any questions regarding the position, please contact Andreas Antefelt ([email protected]).