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Scientist I - Machine learning methods for multimodal clustering of brain cells

Department: 2210 - Informatics and Data Science
Location: Seattle

Scientist I - Design Modern Machine Learning 

The Allen Institute for Brain Science is engaged in a major effort to characterize and understand cell types of the brain. As part of this initiative, the Institute has been generating standardized, high-throughput recordings of neuronal anatomy, physiology, and gene expression in recent years. Principled, data driven analyses are necessary to facilitate discovery from these large, multi-modal datasets.

We are seeking to fill a Scientist level position to design modern machine learning methods for the characterization of cortical neurons, with an emphasis on Bayesian inference for models involving artificial neural networks. In addition to datasets curated by the well-established mouse cortical cell types program, the unique human cortical cell types project at the Institute continues to ramp up data generation. This position will work with interdisciplinary teams consisting of quantitative and experimental scientists on high-priority modeling and analysis problems that include (i) statistical descriptions of cell identity, (ii) deep learning based joint analysis of multi-modal datasets, and (iii) non-parametric mixture models.

JOB RESPONSIBILTIES

  • Develop statistical models of cell identity.
  • Develop novel Bayesian inference algorithms for multi-modal data.
  • Participate in a highly interactive and multidisciplinary environment.
  • Publish/present findings in peer-reviewed journals/scientific conferences.

BASIC QUALIFICATIONS

  • PhD in computer science, neuroscience, physics, mathematics, engineering, or related field.
  • Experience with Bayesian modeling and inference.

SCIENTIST I:

0-2 years postdoctoral experience in computational science

PREFERRED QUALIFICATIONS:

  • Ability to meet aggressive timelines and deliverables in a collaborative environment.
  • Ability to work in a collaborative environment.
  • Experience in computational neuroscience is preferred.
  • Experience in high performance computing is preferred.
  • Strong publication record.
  • Experience in pursuing research projects in collaborative fashion.
  • Proven independent thinking and flexibility.
  • Strong written and verbal communication skills.
  • Experience with GPU-based deep learning is preferred.
  • Experience in computational neuroscience is preferred.

 

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