Postdoc: Computational Neuroscience & Neural Dynamics

UNIVERSITY OF ARIZONA; Tucson (AZ); On-site; USD 65,000 - 90,000; Full time

Benefits offered by this job

Health insurance

Paid vacation

Tuition reduction

Job summary

The University of Arizona invites applications for a Postdoctoral Research Associate in Neuroscience to develop theoretical and computational frameworks for neural network dynamics, focusing on inhibitory control of state transitions. Candidates from quantitative fields will work with experimentalists and access rich electrophysiological data and behavior.

Applicants should have a PhD in a quantitative discipline and strong skills in mathematical modeling and programming.

Qualifications

• PhD or equivalent in engineering, applied mathematics, physics, computational neuroscience, computer science, quantitative biology, or related quantitative field by start date.
• Demonstrated research experience in mathematical modeling, computational analysis, dynamical systems, control, or closely related area.
• Evidence of ability to conduct rigorous independent research.

Responsibilities

• Develop mathematical, computational, and/or control-theoretic models of recurrent neural dynamics.
• Analyze large-scale electrophysiological and behavioral datasets and relate to model predictions.
• Use perturbations to distinguish mechanistic models and identify governing variables.
• Investigate stability, metastability, attractor structure, controllability, and state transitions.
• Collaborate closely with experimental neuroscientists to design analyses and tests of predictions.
• Present findings at meetings and contribute to publications.

Skills

Quantitative reasoning

Nonlinear dynamics

Control theory

Programming (Python)

Programming (MATLAB)

Programming (Julia)

Programming (C/C++)

Independent research

Education

PhD or equivalent by start date

Tools

Python

MATLAB

Julia

C/C++

Job description

The University of Arizona invites applications for a Postdoctoral Research Associate in Neuroscience to develop theoretical and computational frameworks for neural network dynamics, focusing on inhibitory control of state transitions. Candidates from quantitative fields will work with experimentalists and access rich electrophysiological data and behavior.

Applicants should have a PhD in a quantitative discipline and strong skills in mathematical modeling and programming.