Please note that this is a 50 - 100% variable part-time career appointment.

UCSD Layoff from Career Appointment: Apply by 05/17/19 for consideration with preference for rehire. All layoff applicants should contact their Employment Advisor.

Special Selection Applicants: Apply by 05/21/19. Eligible Special Selection clients should contact their Disability Counselor for assistance.

The Department of Cognitive Science promotes the study of learning, perception, action, and interaction in the physical, material, social, and cultural world. The approach is inter-disciplinary employing ideas and techniques from a number of contributing disciplines, including anthropology, communications, computer science, ethology, electrical engineering, linguistics, neurology, neurosciences, philosophy, psychology, rhetoric, and sociology.

The interdisciplinary cognitive science community on campus is integral to cognitive science as practiced at UCSD. The department offers an interdisciplinary Ph.D. in addition to the departmental Ph.D., with participation by members of the Departments of Anthropology, Biology, Cognitive Science, Communication, Computer Science and Engineering, Linguistics, Music, Neuroscience, Philosophy, Psychiatry, Psychology, and Sociology.

The Kutas Cognitive Electrophysiology Lab studies how meaning is organized, accessed, and constructed in the brain. More specifically, we focus on understanding how context shapes language and memory processing. Our studies track these cognitive and neural processes in both healthy and clinical individuals across the adult lifespan. We do this primarily by assessing patterns of brain waves recorded at the scalp as well as reaction times to various visual and auditory stimuli.

The programmer will be working in the lab's various scientific computing ecosystems that include, but are not limited to, Python, R,and MATLAB, running in virtual environments under a Centos 7 linux and Mac OS. Most projects involve test-driven Python 3.X scientific computing package development. Under direct supervision, applies professional bioinformatics concepts and computational procedures to complete small projects or portions of projects. Will begin contributing immediately on assignments of limited scope and complexity. Follows standard programming procedures to analyze situations and data from which answers can be readily obtained.

• Skilled in scripting and programming Python 3.6+ for data analysis with pytest, NumPy, SciPy, Pandas, statsmodels, matplotlib, Jupyter Notebooks/Lab.

• Basic knowledge of applications programming and web development. Knowledge of test-driven scientific computing package development in conda virtual environments with Python.

• Proven knowledge of data visualization. Knowledge of R statistical modeling. Knowledge of parallel processing, profiling, and optimization. Able to modify, patch, and extend the lab's mkpy and fitgrid Python code base (github.com/kutaslab).

• Strong project management skills. Basic knowledge of version control with git, Python package installation tools, validation and open-source distribution.

• Skilled with setuptools, sphinx documentation generation. Travis Continuous Integration on github, and package distribution via github, PyPl and Anaconda Cloud.

• Demonstrated knowledge of bioinformatics methods and data structures. Knowledge of high-performance computing and optimization for large (~ 2-100GB) data processing in Python.

• Ability to communicate technical information in a clear and concise manner. Ability to describe and document bioinformatics programming concepts, applications, user guides and manuals in written and spoken English.

• Interpersonal skills in order to work with both technical and non-technical personnel at various levels in the organization. Able to take and follow direction and respond positively to constructive criticism from lab personnel.

• Self motivated, able to learn quickly, meet deadlines and proven problem solving skills. Able to work independently and constructively in small groups with diverse collaborators. Able to learn and apply new technical material and basic best practices for open-source scientific computing and data analysis.

• A background check is required.

• Must complete Collaborative Institutional Training Initiative (CITI) Human Subjects Research training.