Neuroscience + AI Intern (PhD, Fall 2025)

We are seeking exceptional interns to join our cutting-edge research at the intersection of physiological measurement, computational neuroscience, and next-generation media experiences. You will have the opportunity to develop novel approaches to measuring user engagement through cardiovascular dynamics, neural activity, and other biosignal analysis to enable personalized, adaptive media content. As an intern, you will work closely with our team of researchers and scientists to advance the frontier of engagement-aware media systems that leverage AI and foundation models to adapt in real-time to user state and preferences derived from physiological data. Along with your solid technical skills, candidates should demonstrate problem-solving and analytical abilities, good communication and collaboration skills, a curiosity for how and why things work as they do, and a passion for understanding human perception and engagement with media. You have a desire to bring in new ideas and are open to learning from others and working in a team environment focused on transforming the future of entertainment experiences through AI-driven physiological understanding. You may succeed in this role if you are a PhD candidate in neuroscience, biomedical engineering, computer science, or related fields, and you are excited about bridging physiological measurement with AI and media technology to create more engaging and personalized experiences. Currently pursuing a PhD degree in neuroscience, computational neuroscience, biomedical engineering, computer science, electrical engineering, cognitive science, or a related field. Strong programming and prototyping skills in Python, Matlab, or similar languages with experience in signal processing, time-series prediction and analysis, and AI/ML frameworks (PyTorch, TensorFlow). Familiarity with physiological signal acquisition and analysis, particularly cardiac signals (ECG, PPG, HRV), electrodermal activity (EDA) and EEG measurements. Experience with machine learning techniques and algorithms, particularly deep learning, transfer learning, and foundation models applicable to physiological data and temporal modeling. Understanding of AI model development including data preprocessing, feature engineering, model training, and evaluation for biosignal applications. Understanding of experimental design, hypothesis testing, and collection of perceptual and physiological data in controlled settings. Analytical skills and the ability to manipulate, visualize, and extract meaning from complex physiological and behavioral datasets. Excellent communication and teamwork skills. Ability to work independently and take initiative on complex, interdisciplinary problems involving AI and human physiology. Experience developing foundation models or large-scale representation learning for physiological or biomedical data. Experience with real-time gaming/simulation engines such as Unity or Unreal for creating virtual experimental environments. Experience with edge measurement and ML deployment techniques for real-time physiological monitoring and engagement prediction. Work collaboratively with our team to design and implement experiments measuring cardiovascular dynamics (heart rate variability, PPG) and autonomic physiology (EDA) during media consumption across different content types and viewing contexts. Develop EEG-based neural signature models for media components and events combining naturalistic media stimuli with AI-based content analysis. Create biosignal transfer learning approaches that establish robust mappings between high-fidelity neural signatures and accessible physiological measures from consumer wearable devices. Build foundation models for physiological data representation that can generalize across individuals, devices, and measurement contexts to enable scalable engagement prediction systems. Implement temporal engagement models to predict user state trajectories and optimize content adaptation timing for sustained engagement across diverse media experiences. Develop multimodal AI systems that integrate physiological signals, content features, and contextual information to predict and enhance user engagement in real-time media applications. Leverage large-scale physiological datasets to train foundation models that capture universal patterns in human engagement responses while preserving individual personalization capabilities. Contribute to the development of research papers, patents, and technical presentations advancing the field of AI-driven and engagement-aware media systems. Working towards a PhD degree in neuroscience, biomedical engineering, computer science, or related field; recent graduates within six months of graduation are also eligible to apply.