Bosch R&D in Palo Alto, California, USA is looking for excellent candidates with expertise in probabilistic planning under uncertainty and decision making for a position in the automated driving team.  

 

Degree Level: M.S. with at least 3 years of prior experience or Ph.D.

 

Major(s): Computer Science, Engineering, or a related field.

 

Your Duties and Tasks:

Perform research, develop, implement and evaluate algorithms in one or more of the following fields:

- Planning under uncertainty, probabilistic approaches in robotics as it is applicable to decision making for automated/autonomous vehicles

- Machine learning techniques for novel driver assistance applications or vehicle data analytics

- Novel ways for vehicle decision making, including methods like Bayesian risk analysis in uncertain environments

 

Skills / Job Requirements:

- Excellent knowledge and proven expertise in Planning under uncertainty/Probabilistic approaches in robotics

- Working knowledge of recent machine learning algorithms (Bayesian inference, (PO)MDP planning, etc.) as well as optimization techniques

- Excellent C++ programming expertise required, Python programming is a plus

- Proven system integration and software architecture skills

- Knowledge of Linux, and development on Linux systems preferred

- The ability to develop, understand and implement complex algorithms efficiently and correctly

- Experience with modern software engineering tools

- Experience working independently in a large software setting

- Experience working on robot and/or automotive electronics hardware a plus, as is experience with simulation environments and ROS

- Excellent communication skills and demonstrate a proven ability to multitask and deliver on challenging software development tasks

 

Details and Online Application:

 

 

Thomas Glaser