Postdoc: Quantum & Classical Dynamics of Decision Making

Indiana University; Bloomington (IN); On-site; USD 51,000 - 69,000; Full time

Job summary

Indiana University Bloomington is seeking a Postdoctoral Researcher to advance computational models of decision making that blend quantum and classical approaches within open-system dynamics. The role emphasizes theory development, computational modeling, and experimental validation, with collaboration across psychology and quantitative science communities.

The successful candidate will contribute to model testing, data analysis, and dissemination of findings, with strong mentorship and

Qualifications

• Ph.D. preferred in psychology or cognitive science; strong quantitative background required.
• Demonstrated expertise in mathematical or computational modeling and dynamical systems.
• Proficiency in Python, MATLAB, or R for computational work.
• Strong quantitative, analytic, and scientific writing skills.

Responsibilities

• Develop computational models of dynamic judgment and decision making using quantum probability, open-system dynamics, and classical Markov processes.
• Derive and test predictions distinguishing quantum vs. classical dynamics and evidence evolution.
• Design behavioral experiments probing interference, oscillation, and response-time phenomena.
• Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, or R.
• Lead and co-author manuscripts and conference presentations.

Skills

Mathematical modeling

Computational modeling

Python

MATLAB

R

Dynamical systems

Education

Ph.D. in Psychology or Cognitive Science

Tools

Python

MATLAB

R

Job description

Indiana University Bloomington is seeking a Postdoctoral Researcher to advance computational models of decision making that blend quantum and classical approaches within open-system dynamics. The role emphasizes theory development, computational modeling, and experimental validation, with collaboration across psychology and quantitative science communities.

The successful candidate will contribute to model testing, data analysis, and dissemination of findings, with strong mentorship and