Postdoctoral Research Associate, Neuroscience

The University of Arizona • Tucson, AZ

1d • Onsite

About The Position

The Maurer laboratory at the University of Arizona is seeking a postdoctoral research associate to develop theoretical and computational approaches for understanding how biological neural networks organize activity through nonlinear dynamics, feedback, and constraint. A central question is how inhibitory circuitry reshapes the set of states and transitions available to a recurrent network: not simply by reducing excitation, but by dynamically restricting, routing, and stabilizing trajectories through neural state space. We are interested in whether relatively low-dimensional inhibitory or feedback control signals can reorganize high-dimensional activity, alter attractor structure and metastability, regulate transitions among network states, and make flexible computation possible in circuits that would otherwise be unstable or chaotic. The position is intentionally interdisciplinary. We welcome candidates from control theory, nonlinear dynamical systems, applied mathematics, electrical or biomedical engineering, physics, computational science, and related quantitative fields. Prior neuroscience experience is not required. The researcher will work closely with experimental neuroscientists and have access to rich electrophysiological datasets including neuronal spiking, local field potentials, EEG, behavior, and circuit perturbations. The goal is not merely to fit models to neural data, but to develop mechanistic frameworks that generate discriminating predictions and can be tested against experimental interventions. The Department of Neuroscience at the University of Arizona advances education and research across the interdisciplinary field of neuroscience, encompassing areas such as nervous system development and physiology, behavior, genetics, pharmacology, computational modeling, and neurological and psychiatric diseases. Faculty contribute to a collaborative neuroscience community across the University and actively engage in the interdisciplinary graduate program in Neuroscience and the undergraduate major in Neuroscience and Cognitive Science. OutstandingUA benefits includehealth, dental, vision,and life insurance; paidvacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for theemployee and qualified family members; access to UA recreation and culturalactivities; and more! The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocationservices, please visit talent.arizona.edu

Requirements

• PhD or equivalent doctoral degree in engineering, applied mathematics, physics, computational neuroscience, computer science, quantitative biology, or a related quantitative discipline by the start date.
• Demonstrated research experience in mathematical modeling, computational analysis, dynamical systems, control, or a closely related area.
• Evidence of the ability to conduct rigorous independent research.
• Strong quantitative reasoning and the ability to formulate biological questions as tractable mathematical or computational problems.
• Knowledge of nonlinear dynamical systems, control theory, network dynamics, state-space methods, system identification, stochastic processes, or related quantitative approaches.
• Ability to develop, simulate, and critically evaluate mechanistic models rather than relying solely on descriptive data analysis.
• Scientific programming ability in Python, MATLAB, Julia, C/C++, or a comparable environment.
• Ability to work independently while communicating effectively across disciplinary boundaries.
• Strong written and oral scientific communication skills.

Nice To Haves

• Experience with nonlinear dynamics.
• Experience with control theory and feedback systems.
• Experience with bifurcation or stability analysis.
• Experience with attractor and metastable dynamics.
• Experience with controllability and observability.
• Experience with state-space modeling.
• Experience with system identification.
• Experience with reduced-order modeling.
• Experience with recurrent, reservoir, echo-state, liquid-state, or chaotic neural networks.
• Experience with network dynamics.
• Experience with time-series analysis.
• Experience with stochastic processes.
• Experience with computational modeling of biological systems.
• Experience with analysis of electrophysiological data.
• Experience translating theoretical models into experimentally testable predictions.
• Prior neuroscience experience is not required.

Responsibilities

• Develop mathematical, computational, and/or control-theoretic models of recurrent neural dynamics, with particular emphasis on how inhibition and feedback constrain reachable network states and transitions.
• Analyze large-scale electrophysiological and behavioral datasets and connect model predictions to neuronal spiking, population activity, local field potentials, EEG, and behavior.
• Use experimental perturbations to distinguish among competing mechanistic models and identify the variables or constraints that govern network organization.
• Investigate stability, metastability, attractor structure, controllability, state transitions, system identification, and reduced-order descriptions of neural activity.
• Collaborate closely with experimental neuroscientists to design analyses and experiments that provide strong tests of theoretical predictions.
• Present findings at scientific meetings, prepare manuscripts for peer-reviewed publication, and contribute to an interdisciplinary research environment.

Benefits

• health, dental, vision, and life insurance
• paid vacation, sick leave, and holidays
• UA/ASU/NAU tuition reduction for the employee and qualified family members
• access to UA recreation and cultural activities

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree