- 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.
