Company Description

　　As one of the largest North American automotive suppliers, Bosch develops Driver Assistance functions like Adaptive Cruise Control (ACC), Predictive Emergency Brake Systems (PEBS), Lane Departure Warning/Keeping Systems (LDW, LKS), Predictive Pedestrian Protection, Advanced Parking Assistance, and many more. For these functions Bosch has all necessary sensors in our portfolio (e.g. radar, video, ultrasonic sensors,). The XC division develops state of the art solutions as well as advanced features leading to the future of cockpit electronics and Assisted and Automated Driving

　　We develop state of the art systems as well as advanced features leading to partly/highly automated driving. Join us to become part of the exciting and growing field of Driver Assistance.

　　Job Description

　　Perform vehicle application for parking calibration sets:

　　Create/Define the variants of the projects with SharCC PMA

　　Parameterize vehicle specific dimensions, sensor locations and display regions utilizing information from SPR and CAD files

　　Flash development ECUs for application related work

　　Set-up and maintain vehicle specific configs for data collection in CANape & CANoe

　　Create calibration filters to eliminate disturbing echo reflections from surroundings of the sensor caused by vehicle/bumper attachments

　　Capture and analyze traces for various park assist objects in vehicle

　　Utilize Differential GPS equipment to create and validate the vehicle model

　　Optimize the Power Steering and Braking Parameters

　　Calibrate the parking parameters for parallel and perpendicular auto park feature

　　Perform vehicle application for Object Classification

　　Support parameter selection for software features within the object classification components.

　　Analysis of system signals for reported defects by customer or internal defects found during vehicle validation tests.

　　Analyze and understand ultrasonic sensor echo attributes and filtering in both graphical and analytical data tooling. Key tools for analysis include Vector CANape, CANoe, Excel and custom tools for viewing Advance Drivers Assistance System (ADAS) object data.

　　Comprehend object classification algorithms to allow tuning of performance-based parameters for optimal system performance.

　　Perform root cause analysis on performance issues to identify failing algorithm or calibration parameters.

　　Understand the relationships between multiple calibration parameters and the possible influence of changes to other parts of the system.

　　Develop test plans so in-vehicle teams can verify object classification and localization testing to ensure the system functions to customer requirements.

　　Create technical presentations of system limitations and failure root cause analysis.

　　Perform vehicle application for Simulation Tasks

　　Perform simulations using Automotive Data and Time Triggered Framework ( ADTF ) Development Environment

　　Utilize predefined Jenkins network jobs to process vehicle recorded data into desired data formats for analysis and facilitate simulation of traces.

　　Build and manage code simulation projects within Visual Studio environment for use of calibration change analysis via trace simulation.

　　Contribute to improvements and development of tools and processes utilizing Python and C/C++ Assist in creation of tool user guides and process documentation

　　Potential to use ML Models to train/tune sensor data in a simulation environment and improve Algorithm Software performance.

　　Qualifications

　　Basic:

　　Students majoring in Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering or similar.

　　Good team and communication skills to fit within a committed and very cooperative team

　　Able to work independently in an organized and structured fashion

　　Must be currently enrolled in an accredited university

　　Pursuing Bachelor or Master degree with at least one semester completed

　　Good software code exposure/ programming experience in C or Python (academic) experience with software developments

　　Exposure to ML models for eg. Neural Networks, TensorFlow, XGBoost.

　　Minimum of overall GPA of at least 3.0

　　Must be a minimum of 18 years of age

　　Eligible to work without sponsorship or having 3 years of full-time work experience.

　　Preferred:

　　Experience with Matlab / State flow, Simulink Experience with Vector tooling (e.g. CANape, CANalyzer)Knowledge of "Driver Assistance" functions and sensor technologies

　　Additional Information

　　All your information will be kept confidential according to EEO guidelines.