Computational Neuroscientist

Allen Institute; Seattle (WA); On-site; USD 87,000 - 108,000; Full time

Benefits offered by this job

Relocation assistance

Visa sponsorship possible

Job summary

The Allen Institute in Seattle seeks 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 computation from data, across thousands of neurons and multiple brain regions.

Ideal candidates have deep expertise in applied math or statistics, fluency in modern machine learning for science, and enjoy close collaboration with experimentalists to guide new experiments and

Qualifications

• PhD in neuroscience, physics, applied mathematics, statistics or related field; or equivalent combination of degree and experience.
• Demonstrated 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 many interacting neurons that can be fit directly to connectivity and neural activity data.
• Derive coarse-grained descriptions of these models, reducing them to a small number of variables that reveal connectivity features governing neural dynamics.
• Characterize the space of models consistent with a given dataset, and determine how connectivity constrains that space.
• Develop theories for how different perturbations narrow that space, and identify the most informative experiments to perform next.
• Collaborate with experimentalists to turn theory into testable hypotheses about neural computation.
• Present results to collaborators and the wider scientific community through talks, conferences, and publications.

Skills

Scientific computing

Modern machine learning

Collective dynamics theory

Education

PhD in neuroscience/physics/ mathematics/ statistics or related

Job description

The Allen Institute in Seattle seeks 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 computation from data, across thousands of neurons and multiple brain regions.

Ideal candidates have deep expertise in applied math or statistics, fluency in modern machine learning for science, and enjoy close collaboration with experimentalists to guide new experiments and