The Enigma Project at Stanford University School of Medicine aims to understand the computational principles of natural intelligence using AI. The project seeks to create a foundation model of the brain by leveraging advances in neurotechnology and machine learning, to capture the relationship between perception, cognition, behavior, and brain activity dynamics. This initiative will provide insights into brain algorithms and help align AI models with human-like neural representations.
Requirements
- At least 2+ years of practical experience in training, fine-tuning, and using multi-modal deep learning models
- Strong programming skills in Python and deep learning frameworks
- Experience with large-scale distributed model training frameworks (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)
- Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
- Experience in processing and analyzing large-scale, high-dimensional data of different sources
- Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
- Familiarity with big data and MLOps platforms (e.g. MLflow, Weights & Biases)
Responsibilities
- Design and implement large-scale multimodal deep learning architectures that relate sensory inputs to neuronal correlates of perception, action, and cognition
- Develop novel computational approaches for training and optimizing frontier models on unprecedented amounts of neural data
- Provide technical leadership in distributed training systems and model optimization techniques
- Guide cross-functional teams in establishing technical frameworks and evaluation metrics for brain foundation models
- Stay ahead of the latest developments in machine learning and neuroscience, and propose innovative solutions to advance the project's goals
- Training frontier multi-modal models on large-scale data of neuronal recordings that relate sensory input to neuronal correlates of perception, action, cognition, and intelligence.
Other
- Ph.D. in Computer Science, Machine Learning, Computational Neuroscience, or related field plus 2+ years post-Ph.D. research experience
- Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multi-modal modeling
- Demonstrated ability to lead research projects and mentor others
- Ability to work effectively in a collaborative, multidisciplinary environment
- Bachelor's degree and five years of relevant experience, or combination of education and relevant experience.
