ML Research Scientist — Multimodal Genomics & Disease Modeling

Allen Institute; Seattle, Northern (WA, KY); Hybrid; USD 87,000 - 107,000; Full time

Benefits offered by this job

Relocation assistance

Visa sponsorship

Job summary

The Allen Institute in Seattle seeks a Scientist I to design modern machine learning methods that integrate multimodal data and illuminate Alzheimer’s disease trajectories. You will develop deep generative models and Bayesian algorithms to advance mechanistic understanding of brain pathology.

We welcome candidates with a Ph.D. in a relevant field and a track record in AI for biomedical data, with preference for experience in causal inference or gene regulation; hybrid onsite/remote work in

Qualifications

• 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.

Responsibilities

• 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.
• Publish/present findings in peer-reviewed journals/scientific conferences.

Skills

Deep learning architectures

Bayesian inference

Causal inference

Gene regulation

Education

PhD in Computer Science or related field

Job description

The Allen Institute in Seattle seeks a Scientist I to design modern machine learning methods that integrate multimodal data and illuminate Alzheimer’s disease trajectories. You will develop deep generative models and Bayesian algorithms to advance mechanistic understanding of brain pathology.

We welcome candidates with a Ph.D. in a relevant field and a track record in AI for biomedical data, with preference for experience in causal inference or gene regulation; hybrid onsite/remote work in