Scientist:Neural Circuits Theory & Data-Driven Brain Models

ClearCompany Talent Management Software; Seattle (WA); On-site; USD 87,000 - 108,000; Full time

Benefits offered by this job

Relocation assistance

Visa sponsorship potential

Job summary

The Allen Institute for Brain Science in Seattle is seeking a Scientist to develop data-driven theories of how circuit structure shapes neural computation. You will use large-scale connectivity atlases and brain-wide recordings to distill theories of computation from data produced at unprecedented scale.

The role requires deep expertise in applied mathematics or statistical physics and fluency in modern machine learning for science, with close collaboration to experimentalists using

Qualifications

• PhD or equivalent combination of degree and experience.
• Ability to build theories of collective dynamics in systems of many interacting elements.
• Strong scientific-computing skills and familiarity with modern machine learning.

Responsibilities

• Develop mechanistic models of interacting neurons that can be fit to connectivity and neural activity data.
• Derive coarse-grained descriptions of models to identify key features governing neural dynamics.
• Characterize model spaces consistent with datasets and how connectivity constrains them.
• Develop theories for different perturbations to guide informative experiments.
• Collaborate with experimentalists to test hypotheses of neural computation.
• Present results to the scientific community through talks and publications.

Skills

Theoretical modeling

Scientific computing

Machine learning

Education

PhD in neuroscience/physics/ mathematics/statistics

Equivalent degree/experience

Job description

The Allen Institute for Brain Science in Seattle is seeking a Scientist to develop data-driven theories of how circuit structure shapes neural computation. You will use large-scale connectivity atlases and brain-wide recordings to distill theories of computation from data produced at unprecedented scale.

The role requires deep expertise in applied mathematics or statistical physics and fluency in modern machine learning for science, with close collaboration to experimentalists using