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; 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; 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 relocations
services, please visit talent.arizona.edu
Duties & 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.
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.
Minimum Qualifications
- 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.
Preferred Qualifications
- 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.
FLSA: Exempt
Full Time/Part Time: Full Time
Number of Hours Worked per Week: 40
Job FTE: 1.0
Work Calendar: Fiscal
Job Category: Research
Benefits Eligible: Yes - Full Benefits
Rate of Pay: NIH salary guidelines, Depends on Experience.
Compensation Type: salary at 1.0 full-time equivalency (FTE)
Type of criminal background check required: Name-based criminal background check (non-security sensitive)
Number of Vacancies: 1
Contact Information for Candidates
Andrew "Drew" Maurer Associate Professor, Neuroscience drew@arizona.edu
Open Until Filled: Yes
Documents Needed to Apply: Curriculum Vitae (CV), Cover Letter, and One Additional Document
Special Instructions to Applicant
Please include Statement of Research Interest in the additional document spot.
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