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. Knowledge, Skills, and Abilities 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.