Job Description

Company Description

The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), a company with over 70 billion euro revenue, 400,000 employees worldwide, a very diverse product portfolio, and a history spanning over 125 years. The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence (for example, human-assisted AI, natural language processing, robotics, 3D perception, and AI platform), energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design.

Our global research on human-machine intelligence focuses on Big Data Visual Analytics, Explainable AI (XAI), Audio Analytics, Natural Language Processing, Knowledge Engineering, XR/AR/MR, 3D Perception, and Cloud Robotics. We develop intelligent and trustworthy AIoT solutions to enable inspiring UX for Bosch products and services in application areas such as autonomous driving, driver assistance systems (ADAS), robotics, smart manufacturing, health care, smart home and building solutions.

As a part of the global Human Machine Collaboration (HMC) research unit, our Visual Analytics & eXplainable AI group is responsible for shaping the future industrial AI experience for Bosch products and services by combining cutting-edge technologies of machine learning, data analysis and interactive visualization. We research and develop scalable, transparent, and intelligent big data analytic solutions (e.g., audio, images, sensor logs) for various domains including Industry 4.0 (I4.0), IoT, autonomous driving, connected vehicles, etc. We also actively collaborate with leading groups in academia and industry to promote research ideas and publish research findings in internationally renowned conferences and journals, e.g., ICASSP, Interspeech, IEEE Signal Processing Magazine, IEEE VIS, TVCG, SIGKDD.

Job Description

• Develop state-of-the-art Audio AI solutions using combination of classical signal processing and machine/deep learning-based approaches
• Research and develop solutions for few-shot learning of rare sound events, anomaly detection for machine health monitoring, active learning, domain adaptation to new and unforeseeable environments/classes on top of state-of-the-art techniques for audio event/scene recognition
• Collaborate with others to integrate audio AI modules into the prototype systems
• Summarize research findings in high-quality paper and/or patent submissions

Qualifications

Required:

• Currently enrolled as PhD student in Computer Science, Electrical Engineering, Data Science, or related fields
• 2+ years programming experience, proficiency in Python, Numpy, Scipy, Pandas, PyTorch, Keras, Tensorflow
• Broad knowledge of machine- and deep-learning algorithms and principles and state-of-the-art methods

Preferred

• Knowledge of digital signal processing principle and methods (DFT, filter-banks)
• Publication record in top machine learning and audio analytics venues (e.g., ICASSP, Interspeech, AAAI, ICML)

Additional Information

• The U.S. base salary range for this intern position is $34.00-$66.00 hourly. Within the range, individual pay is determined based on several factors, including, but not limited to, type of degree, work experience and job knowledge, complexity of the role, type of position, job location, etc. Your Hiring Manager can share more details about the specific salary range for this position during the interview process

By choice, we are committed to a diverse workforce - EOE/Protected Veteran/Disabled.

BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics) Initiatives

• FIRST Robotics (For Inspiration and Recognition of Science and Technology)
• AWIM (A World In Motion)

Indefinite U.S. work authorized individuals only. Future sponsorship for work authorization unavailable.