Tarun Bollineni
Fähigkeiten und Kenntnisse
Werdegang
Berufserfahrung von Tarun Bollineni
Vehicle motion control, ADAS, Autonomous Driving, Product owner, System Architecture, Systems Engineering
- 7 Monate, Apr. 2018 - Okt. 2018
Master Thesis Student
ZF Friedrichshafen AG, Standort Friedrichshafen
Topic : Model Predictive Control (MPC) as a function for Trajectory Control during High Dynamic Vehicle Maneuvers considering Actuator Constraints
- 7 Monate, Sep. 2017 - März 2018
Engineering Intern
ZF Friedrichshafen AG, Standort Friedrichshafen
Topic : Comfort based driving functions for highly dynamic autonomous driving scenarios
-Technical analysis of various output management and document management software available and selection of a software with features which matches the requirements of the company. -Study and analysis of assessment tools available for the processing of information from databases, SAP systems, web services and user inputs.
- 6 Monate, Feb. 2015 - Juli 2015
Engineer
Emage Vision
-Involved in analysing raw images, disseminated, reviewed and managed bugs/issues in database. -Implemented and integrated a Machine vision System with GigE Baseler ace-A2500-14gm camera, ADLINK Neon1020 Smart camera , I/O card and other systems.
- 5 Monate, Feb. 2014 - Juni 2014
Engineering Intern
Robert Bosch, Bangalore, India
-Up-gradation of PLC, Vision Camera and Bolting system to avoid human error and obsolescence management. -Integration and configuration of a system with Cognex insight 5100 series vision camera, Bolting SE350 controller and Bosch Rexroth PLC VDP controller.
Ausbildung von Tarun Bollineni
- Bis heute 9 Jahre und 10 Monate, seit Okt. 2015
Automotive Software Engineering
Techniche Universität Chemnitz
Machine Learning, Computer Vision, AUTOSAR, Robotics, Design of Software for Embedded Systems
- 4 Jahre, Aug. 2010 - Juli 2014
Electronics and Communication Engineering
Visvesvaraya Technological University-(VTU, Belgaum, Karnataka)
Digital signal Processing, VHDL, Micro controllers, Micro Processors, C Programming, Analog and digital circuits, Field theory, Artificial neural networks
Sprachen
Englisch
Fließend
Deutsch
Grundlagen
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