Research Intern AI/ML & DH at Johnson &… · Cambridge

Summary: Johnson & Johnson is a leader in healthcare innovation, dedicated to developing smarter and less invasive treatments. They are seeking a Research Intern in AI/ML and Digital Health to support the development of foundation models for wearable sensor data, contributing to significant advancements in health technology. Responsibilities: - Design and implement self‑supervised learning frameworks for wearable time‑series data - Train foundation models on large‑scale unlabelled multimodal sensor datasets - Develop architectures using transformers, contrastive learning, masked modelling, and cross‑modal attention - Integrate heterogeneous sensors (accelerometer, PPG, ECG, heart‑rate) using multimodal fusion strategies - Evaluate learned representations on downstream health tasks (HAR, sleep, stress, gait, health outcomes) - Contribute to reproducible research outputs, including publications and well‑documented code Required Qualifications: - Completion of Undergraduate Freshman year at an accredited University is required - Currently pursuing a PhD in Machine Learning, AI, Computer Science, Biomedical Engineering, Signal Processing, or related fields - Have a cumulative GPA of 2.8 or higher, which is reflective of all college coursework - Strong programming experience with Python and deep learning frameworks (PyTorch or TensorFlow) - Strong technical background in deep learning for time-series data and self-supervised learning - Experience working with large-scale datasets and research pipelines - Permanently authorized to work in the U.S., must not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future Preferred Qualifications: - Experience with wearable sensor data and multimodal machine learning - Familiarity with transformers, contrastive learning, and masked modelling - Background in physiological signal processing and digital health applications - Exposure to time-series foundation models or large-scale model training Required Skills: Python, Deep learning frameworks, Self-supervised learning Important Skills: Multimodal machine learning, Large-scale datasets Nice-to-Have Skills: Biomedical Engineering, Signal Processing, Physiological signal processing, Digital health applications Benefits: Company sponsored employee medical benefits in accordance with the terms of the plan, Up to 40 hours per calendar year; for employees who reside in the State of Washington, up to 56 hours per calendar year, The Company’s consolidated retirement plan (pension)