Joining the lab

The Computational Cognitive Neuroscience & Psychiatry Lab is looking for highly motivated, energetic undergraduate research assistants to help with several projects. The lab’s research focuses on understanding information processing in human learning, decision making, and episodic memory, and how it is disrupted in psychiatric disorders (anxiety, obsessive compulsive disorder, and depression) – an area recently dubbed computational psychiatry. In carrying out this work, we leverage tools from cognitive neuroscience, experimental and mathematical psychology, computer science, and statistics. We work at the intersection of theory and experiment, building computational models of information processing and testing them using both behavioral data and neuroimaging (fMRI and scalp EEG).

This position involves collecting EEG data, which are (multivariate) electrical potentials recorded at the surface of the scalp, while participants perform various cognitive (e.g. learning, decision making, and memory) tasks. This position is ideal for ambitious early career students (i.e. freshman through junior) interested in neuroscience, signal processing, and getting a glimpse of what graduate school is like. The focus of this particular position is primarily on data collection and not analytics — thus, while seniors are also welcome to apply, they may be better suited in a position that also involves data analysis.

This research experience may be taken for credit (PSYC 479, additional requirements apply).

 

Responsibilities:

-Participant recruitment

-Assistance with EEG data acquisition

10 hours per week time commitment

 

Qualifications:

A major in Psychology, Bioengineering, Neurobiology/Neuroscience, Biology, Computer Science or a related field

-Flexible availability on evenings and weekends

-Strong communication skills and professionalism

-Detail-oriented

 

How to apply:

Interested candidates should send a cover letter describing relevant experience and interests, CV/resume, and an unofficial transcript to Zhen Lin (zhenlin@umd.edu).