Research - Laboratory/Non-Laboratory, Staff/Administrative
Location: Illinois: Chicago
Central knowledge resource for faculty and researchers in Chicago Booth, working closely with faculty to identify, develop, and implement useful computational methods and resources that advance their research.
Assists in the development, implementation, and improvement of application software and methods that can be utilized in analyzing and interpreting data in Chicago Booth. Assists with compiling, debugging, optimizing, profiling and porting codes on RCC compute and storage resources.
Designs tutorials, hands-on workshops, and documentation for researchers and students in Chicago Booth with the help and coordination of other RCC staff.
Keeps abreast of new developments in high-end computing and pro-active in introducing them to the faculty.
Helps faculty with grant proposals by contributing sections of the proposal that describe the interplay between their research and high-end computing resources.
Assists researchers with data management implementations and compliance to funding agencies requirements.
Dedicates a portion of time on research projects or activities for professional development, as approved.
Participates in the product development life cycle, providing professional assistance to the design of front-end applications and database systems back-end schema. Analyzes high-level system specifications and makes sure that all application development standards are met.
Evaluates past and present technologies to help develop new tools. Ensures all the new tools have been through quality control reviews.
Performs other related work as needed.
Ph.D., preferably in the fields of Economics, Finance, Behavioral Science, Statistics, Applied Math or Computer Science with experience applying computer science methodologies in economics, financial management or similar.
Technical Skills or Knowledge:
Proficiency with machine learning techniques and algorithms.
Broad programming skills to support analysis efforts in multiple programming environments.
Numerical computing experience using statistical programming packages, such as Python, Matlab or R.
Fluency in data manipulation and analysis.
Background in a UNIX-based programing environment.
Proficiency with applied statistical skills, such as distributions, statistical testing, regression, etc.
Accomplished in one or more programing languages (such as C++, C, Fortran, Stata, Julia, etc.).
Working knowledge with data visualization tools, such as D3 or GGplot.
Parallel computing experience with MPI and OpenMP.
Familiarity with Natural Language Processing (NLP) methods preferred, recent trends in deep learning and hands-on experience with various Deep Learning methods and technologies such as PyTorch.
Exposure or experience in any of the following a plus: Hadoop, Pig, Hive, SQL coding, relational database structures and query techniques.
Understand and translate researchersâ™ scientific goals into computational requirements.
Excellent communications and presentation skills.
Function as part of an interactive team while demonstrating self-initiative to achieve projectâ™s goals and research computing centerâ™s mission.
Cover letter (preferred)
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Internal Number: JR09797
About University of Chicago (UC)
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