Scientist I - ML/AI algorithms for Multimodal Foundational Models for Gene Regulation
Allen Institute
Summary
We seek to hire a Research Scientist to design modern machine learning methods to integrate multimodal data and describe disease trajectories and contribute to the mechanistic understanding of Alzheimer's disease pathology. The successful candidate will have a strong background in computational biology, and experience developing deep generative models and Bayesian algorithms. In addition, the ideal candidate will either have experience in causal inference or gene regulatory network inference, or has worked on aspects of gene regulation in disease.
Job description
Role Overview
We seek to hire a Research Scientist to design modern machine learning methods to integrate multimodal data and describe disease trajectories and contribute to the mechanistic understanding of Alzheimer's disease pathology. The successful candidate will have a strong background in computational biology, and experience developing deep generative models and Bayesian algorithms. In addition, the ideal candidate will either have experience in causal inference or gene regulatory network inference, or has worked on aspects of gene regulation in disease.
Essential Functions
- 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
Required Education and Experience
- Ph.D. in Computer Science, Applied Math, Engineering, Computational Neuroscience, Computational Biology, or related field, or equivalent combination of degree and experience
- Experience working with recent Deep Learning Architectures/Foundational Models
- Experience with Bayesian modeling and inference
- Experience developing causal models
Preferred Education and Experience
- Experience with current ML models such as score-based diffusion models, multimodal data fusion transformer architectures, or state-space models
- Proficiency with cloud computing and with on-prem clusters
- Strong publication track record
- Proven ability to work independently and manage multiple projects simultaneously while meeting deadlines in a highly collaborative environment
- Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment
Requirements
- Ph.D. in Computer Science, Applied Math, Engineering, Computational Neuroscience, Computational Biology, or related field, or equivalent combination of degree and experience
- Experience working 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
- Proficiency with cloud computing and with on-prem clusters
- Strong publication track record
- Proven ability to work independently and manage multiple projects simultaneously while meeting deadlines in a highly collaborative environment
- Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment
Benefits
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Basic Life Insurance
- 401(k)
- Paid Time Off
- Relocation Assistance
Company overview
The Allen Institute is a premier non-profit research organization founded in 2003 by philanthropist Paul G. Allen, co-founder of Microsoft. Headquartered in Seattle, Washington, the institute is dedicated to answering fundamental questions in biology and accelerating scientific discovery through large-scale, collaborative research projects. The organization operates several major research divisions including the Allen Institute for Brain Science, Allen Institute for Cell Science, and Allen Institute for Immunology. The Allen Institute is renowned for creating comprehensive, open-access data resources and tools for the global scientific community, such as the Allen Brain Atlas, which provides detailed maps of gene expression in the mouse and human brain. Their research approach combines experimental biology with computational analysis and data science to generate transformative insights into complex biological systems. The institute's work has significantly advanced understanding of brain function, cellular biology, and immune system dynamics, making it a leading force in modern biomedical research with a strong commitment to open science and data sharing.