Postdoctoral Research Associate, Neuroscience

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.

• 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.

• 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.
  
  

• 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.