Postdoctoral Fellow-MSH-13400-342

Appointment Term (and whether reappointment is possible)-1 year

Starting date-TBD

Location of appointment- Center for Computational Psychiatry

55W 125th St., 13th floor

New York, NY, 10027

Lab and department websites- https://icahn.mssm.edu/about/departments/psychiatry

Wages or salary; and benefits-$73,588

The successful candidate will participate in the following aspects of research in the lab, including:

• Designing novel experiments to examine the neurocomputational basis of decision-making.

• Conduct human intracranial physiology and behavioral data collection.

• Conduct computational analyses.

• Report findings in peer reviewed papers, international conferences and relevant research talks

Prior research experience in electrophysiology and model-based behavioral analysis.

Data collection from both healthy controls as well as patients with diagnosis of compulsivity (e.g., OCD); behavioral and neural findings related to the neural basis of decision-making.

PhD, or MD

• 859 - Psychiatry
• ISM, Icahn School of Medicine

A postdoctoral researcher is sought to work on model-based analyses of behavioral and electrophysiological data recorded in human populations. The candidate will work across the newly established research laboratory led by PI Vincenzo Fiore at the Center for Computational Psychiatry and the newly established research laboratory led by PI Andrew H. Smith at the Center for Advanced Circuit Therapeutics. The focus of the work will be on compulsive behaviors.

Several theories postulate that both habitual and goal-oriented decision-making processes are compromised in compulsive behavior. The development of new, more comprehensive computational models of value (i.e., based on reinforcement learning algorithms) and belief update processes (i.e., based on Bayesian inference algorithms) jointly with neural models of cortico-basal ganglia dynamics (i.e., based on neural recordings) can shed a light on these compromised processes, resulting in important tools for precision diagnosis and treatment.

This project will develop and deploy a variety of model-based analyses of choice behavior and neural dynamics, applying these tools for the analysis of behavioral and intracranial data (single units, local field potentials), collected in human populations, with a focus on compulsive behaviors.