Scientist I: Multimodal ML for Gene Regulation & Disease

alleninstitute; Seattle (WA); Hybrid; USD 86,500 - 106,500; Full time

Benefits offered by this job

Relocation assistance

Visa sponsorship

Job summary

The Allen Institute seeks a Scientist I 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,

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
• Experience developing causal models

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
• Participate in a highly interactive and multidisciplinary environment
• Publish/present findings in peer-reviewed journals/scientific conferences

Skills

Deep learning

Bayesian modeling

Causal models

Multimodal data

Team collaboration

Education

Ph.D. in CS/Applied Math/Engineering/Computational Neuroscience/Computational Biology

Tools

Cloud computing

On-prem clusters

Job description

The Allen Institute seeks a Scientist I 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,