Positions available

Multiple research positions are available in the Triplett lab at UCLA focused on statistical machine learning methods for neuroscience and mechanistic models of neural computation and cognition. 

Example project areas

Project area 1: Statistical methods for inferring neural circuit connectivity from optogenetic stimulation experiments.

Project area 2: Reverse-engineering models of cognition, including RNNs for causal structure learning & counterfactual reasoning; testing resulting theories in human intracranial recordings via collaborations.

Project area 3: AI-guided closed-loop stimulation, Bayesian experimental design algorithms, and modelling stimulation-evoked animal behavior.

Project area 4: Causal inference methods for analyzing AI systems and/or biological neural circuits.

Other areas of interest: machine learning for neural encoding and decoding; brain-computer interfacing; connectomics; latent factor analysis, dimensionality reduction, and neural dynamics; internal state estimation; analysis of large-scale electrophysiological and imaging datasets; Bayesian statistics.

Candidates should have strong computational skills and a background in a quantitative discipline (computer science, statistics, physics, electrical engineering, mathematics, etc). A background in neuroscience is not required. If interested, please send a CV and description of past and future research interests to marcustriplett@ucla.edu.

Marcus A. Triplett

Assistant Professor of Neurobiology

David Geffen School of Medicine

University of California, Los Angeles