About the Role:

Bosch Research and Technology in Pittsburgh is seeking a motivated student intern to join our research team. This position is part of an exciting effort to explore the potential of intelligent sensing for digital health, from ambient and wearable sensors to the development of physiological foundation models. You will contribute to building, testing, and refining machine learning models for health sensor data analysis and work alongside experienced researchers to translate innovative ideas into practical applications for monitoring and decision support/augmentation.

Responsibilities:

• Assist in the research and development of multi-sensor data fusion techniques using data from ambient and wearable sensors, including time-series data, and other health-related signals.
• Support the design, training, and evaluation of multi-modal machine learning models for health monitoring, caregiver dashboards, and proactive diagnostic systems.
• Collaborate with cross-functional teams to help set up, prototype, and test new health monitoring solutions.
• Document and communicate research findings clearly, contributing to internal reports, presentations, and potential publications or patents.
• Stay up-to-date with emerging technologies in digital health and assist in exploring novel applications (e.g., elderly care technologies).

Required:

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Computer Engineering, Biomedical Engineering, Data Science, or a related technical field.
• Minimum GPA of 3.0
• Broad knowledge of machine- and deep-learning algorithms and principles and state-of-the-art methods.
• Proficiency in Python and experience with ML frameworks such as Py Torch or Tensor Flow.
• Familiarity with sensors, IoT devices, or cyber-physical systems.
• Strong communication skills and an ability to work collaboratively in a research environment.
• Enthusiasm for learning and applying new technologies to solve real-world healthcare challenges.

Desired:

• Experience with health-related research or using raw sensor data (e.g., IMU, radar, ECG, EKG, etc.) in machine learning projects.
• Knowledge of digital signal processing principles and methods for biomedical signals.
• Interest or experience in digital health, aging-in-place technologies, or healthcare data analysis.
• Research experience on Hybrid Models and Neuro-Symbolic AI, including mechanistic modeling and machine learning, using parametric and nonparametric models, methods that compress structured symbolic knowledge to integrate with neural patterns (and reason using the integrated neural patterns).
• Experience with additional programming languages (C, C++) or embedded systems for sensor setup.
• Publication record in top machine learning, signal processing, or digital health venues.

Benefits:

• Hands-on experience with cutting-edge health-sensing and machine learning research.
• Mentorship from experienced researchers in AI, IoT, and cyber-physical systems.
• Opportunity to contribute to real-world projects with potential impact on products and processes.

*Equal Opportunity Employer, including disability / veterans. *

**Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.

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