COMPANY DESCRIPTION
Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together - building a strong collegiality and morale within the university.
Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities - all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.
RESPONSIBILITIES
The candidate will be responsible for conducting research on SOTIF testing, failure analysis, and failure mitigation for ADSs. The aim is to generate critical scenarios that cause motion failures of autonomous vehicles, perform a thorough analysis of the root cause of these failures, and develop new algorithms to mitigate them. Successful candidate will be part of an active research team led by Prof Xie Xiaofei from School of Computing and Information Systems (SCIS), Singapore Management University (SMU).
- Design scenario modeling methods for ADS testing.
- Design algorithms for critical scenarios generation.
- Develop methods for failure analysis and root cause analysis of critical scenarios.
- Develop methods to mitigate failures and improve ADS safety.
- Investigate robust motion planning for ADSs that can tolerate module failures in ADSs.
- Other duties as assigned.
- Minimum Master's degree in Computer Science, Information Technology, Information Systems or closely related disciplines from a reputable institution of higher learning.
- Minimum 5 years of relevant research experience in ADS testing, motion planning, and deep learning.
- Proficiency in programming software/languages such as Python and SVL/CARLA will be positively considered.
- Proven track record and professional experience in ADS testing and motion planning.
- Demonstrated experience and know-how on ADS testing and robust motion planning.
- Excellent working knowledge of some simulators (e.g., CARLA, SVL ) and ADSs (e.g., Apollo, Autoware).
- Technical skills in ADS testing and learning-based motion planning.
- Ability to break down complex problems and design effective solutions.
- Innovating new approaches and thinking outside the box.
- Applicants with research publications in software engineering, intelligent vehicles, and robotics area will be advantageous.
- Ability to work cooperatively as part of a small, agile academic research team is essential.
- Self-motivated individual who can work independently and collaboratively with team members.
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Please note that your application will be sent to and reviewed by the direct employer - Singapore Management University