Wireless Systems Research Intern

The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is part of the global Bosch Group ( www.bosch.com ), a company with over 90 billion euro revenue, 400,000 people worldwide, a very diverse product portfolio, and a history of nearly 140 years. The Research and Technology Center North America (RTC-NA) is committed to providing technologies and system solutions for various Bosch business fields primarily in the areas of Human Machine Interaction (HMI), Robotics, Energy Technologies, Internet Technologies, Circuit Design, Semiconductors and Wireless, and MEMS Advanced Design.

The Bosch wireless sensor and embedded systems research group is looking for an enthusiastic graduate student intern to work on product oriented research. We seek an ideal candidate with good theoretical background and strong desire for practical implementation in the area of Wireless Sensing systems. This internship will provide the candidate with an all round research experience of product development including active contribution in developing requirements specification and algorithm/protocol development leading to proof-of-concept demo.

Responsibilities

Design and develop innovative intelligent wireless solutions with focus on AI/ML.; Design and develop embedded software to implement proof of concept prototypes.; Conduct performance analysis and feasibility studies using simulations, experiments, analytical modeling.; Publish research work in the form of patents and conference/journal publications

Requirements

MS Student in Computer Science or Electrical Engineering; Experience in wireless communication, signal processing and machine learning.; Experienced in using different machine learning libraries and tools (e.g. TensorFlow); Strong C and Python programming skills, embedded programming experience is a big plus; Minimum GPA of 3.0; Duration of Internship: 3-6 months

Nice to have

Knowledge of data augmentation models, synthetic data generation, GANs and transfer learning