The Enigma Project at Stanford University School of Medicine aims to understand the computational principles of natural intelligence using AI, by creating a foundation model of the brain. This role focuses on developing methods for analyzing and interpreting these models to understand how the brain represents and processes information.
Requirements
- Ph.D. in Computer Science, Machine Learning, Computational Neuroscience, or related field plus 2+ years post-Ph.D. research experience
- At least 2+ years of practical experience in training, fine-tuning, and using multi-modal deep learning models
- Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multi-modal modeling
- Strong programming skills in Python and deep learning frameworks
- Background in theoretical neuroscience or computational neuroscience
- Experience in processing and analyzing large-scale, high-dimensional data of different sources
- Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
Responsibilities
- Lead research initiatives in the mechanistic interpretability of foundation models of the brain
- Develop novel theoretical frameworks and methods for understanding neural representations
- Design and guide interpretability studies that bridge artificial and biological neural networks
- Advanced techniques for circuit discovery, feature visualization, and geometric analysis of high-dimensional neural data
- Collaborate with neuroscientists to connect interpretability findings with biological principles
- Mentor junior researchers and engineers in interpretability methods
- Help shape the research agenda of the interpretability team
Other
- Demonstrated ability to lead research projects and mentor others
- Ability to work effectively in a collaborative, multidisciplinary environment
- Demonstrated project leadership experience.
- Demonstrated experience leading and/or managing technical professionals.
- May require travel.
