Healthcare Sensing and Machine Learning Intern at Bosch Group

Pittsburgh, Pennsylvania, United States -

Full Time

Start Date

Immediate

Expiry Date

10 May, 26

Salary

0.0

Posted On

09 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Python, Data Fusion, Health Monitoring, IoT Devices, Signal Processing, Research, Collaboration, Communication, Digital Health, Biomedical Engineering, Caregiver Dashboards, Proactive Diagnostics, Sensor Data, Neuro-Symbolic AI

Industry

Software Development

Description

Company Description We Are Bosch. At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our areas of activity are every bit as diverse as our outstanding Bosch teams around the world. Their creativity is the key to innovation through connected living, mobility, or industry. Let’s grow together, enjoy more, and inspire each other. Work #LikeABosch Reinvent yourself: At Bosch, you will evolve. Discover new directions: At Bosch, you will find your place. Balance your life: At Bosch, your job matches your lifestyle. Celebrate success: At Bosch, we celebrate you. Be yourself: At Bosch, we value values. Shape tomorrow: At Bosch, you change lives. Do you want beneficial technologies being shaped your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. Job Description 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). Qualifications 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 PyTorch or TensorFlow. 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. Additional Information 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. Legal Entity: Robert Bosch LLC

Responsibilities

Assist in the research and development of multi-sensor data fusion techniques and support the design and evaluation of machine learning models for health monitoring. Collaborate with teams to prototype and test new health monitoring solutions.