Scientist I – Neural Circuits Theory

About the role

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 Neural Dynamics accelerator is to understand how the brain generates flexible behavior. We aim to uncover the algorithms—implemented by dynamics in brain-wide neural circuits—that allow animals to build internal models, process information, and choose actions. We are searching for a Scientist to develop data-driven theories of how circuit structure shapes neural computation. The Scientist will use the Institute's large-scale connectivity atlases and brain-wide recordings to understand how the brain's wiring organizes neural dynamics and drives flexible behavior. The position offers a rare opportunity: distilling theories of computation from connectivity data produced at unprecedented scale, together with spike trains from thousands of neurons recorded simultaneously across multiple brain regions, and a wealth of targeted activity perturbations during behavior. The ideal candidate has deep expertise in applied mathematics or statistical physics, fluency in modern machine learning for science, and a genuine interest in how the brain computes. The role requires close collaboration with experimentalists using state-of-the-art anatomical, electrophysiological, and imaging methods. The goal is to make fundamental discoveries about how mammalian circuit architecture gives rise to function, and to generate insights that shape new experiments at the Institute.

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 which features of connectivity — cell-type composition, anatomical projections, the statistical structure of the wiring — govern 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 use them to 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

Requirements

• 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

Physical demands include fine motor movements in fingers/hands to operate computers and other office equipment; repetitive motion with lab equipment.

Qualifications

• Deep interest in foundational questions in neuroscience or biological systems
• Experience building models constrained by or fit to experimental data
• Experience collaborating with experimentalists