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

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

The Allen Institute accelerates science for a healthier world through large-scale research designed to answer some of the most complex questions in biology. Our multi-disciplinary teams generate foundational knowledge, tools, and data to understand how our brain, cells, and immune system work. We share our work openly so others can build on it, move faster, and ask bigger questions. We drive discovery forward and create new possibilities for improving human health.

The goal of the Brain Health accelerator is to revolutionize the treatment of brain diseases by creating the first dual-use molecular tools that can not only reveal information about the diversity of cells in the human brain but also be used to target novel treatments to specific types of cells.

The Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium strives to achieve a deep molecular and cellular understanding of the early pathogenesis of Alzheimer's disease (AD). Leveraging advances in quantitative neuropathology and next-generation single cell molecular profiling technologies we are generating large-scale data sets from well-characterized donors that span the spectrum of AD pathology to comprehensively map human brain cell types across aging and disease.

These data along with tools for their use are made freely and publicly available.

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.

Strong preference will be given to individuals with a track record of both individual and team contributions in solving complex research problems, and experience in cutting-edge computational methodologies applied to biological
-omics, spatial, pathological, and/or clinical metadata.

At the Allen Institute, we believe that science is for everyone - and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly.

We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.

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

Note:

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management's assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

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…