Scientist I - ML/AI algorithms for Multimodal Foundational Models for Gene Regulation

The Allen Institute seeks a Scientist I to develop ML algorithms for multimodal data modeling of disease progression, particularly Alzheimer's. The role involves building Bayesian and causal models, and requires expertise in deep learning and foundational models.

• Develop modern machine learning algorithms to model disease progression from multimodal data (omics, neuropathology, MRI, genetic information, clinical histories)
• Develop Bayesian statistical models of neurodegenerative progression
• Evaluate models that can harmonize multiple cohort information
• Develop causal models of disease progression
• Stay at the forefront of advances in AI for multimodal disease progression modeling
• Participate in a highly interactive and multidisciplinary environment
• Publish/present findings in peer-reviewed journals/scientific conferences

• Ph.D. in Computer Science, Applied Math, Engineering, Computational Neuroscience, Computational Biology, or related field
• Experience with recent Deep Learning Architectures/Foundational Models
• Experience with Bayesian modeling and inference
• Experience developing causal models
• Experience with current ML models such as score-based diffusion models, multimodal data fusion transformer architectures, or state-space models