Understanding the computational principles of natural intelligence using the tools of artificial intelligence and creating a foundation model of the brain
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
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
- Communicate research findings through publications, presentations, workshops and research blogs
Other
- Ability to work effectively in a collaborative, multidisciplinary environment
- Ability to lead research projects and mentor others
- Ph.D. in Computer Science, Machine Learning, Computational Neuroscience, or related field plus 2+ years post-Ph.D. research experience
- Bachelor's degree and five years of relevant experience, or combination of education and relevant experience
- May require travel
