Applied Machine Learning Predoctoral Research Professional
The Center for Applied Artificial Intelligence is recruiting research professionals to tackle important and hard problems in economics and social science. Our research utilizes cutting edge technologies such as CNNs, GANs, and transformer models. We seek real world applications for these technologies, like reducing the discrimination against Black patients in healthcare algorithms and explaining drivers of bias in the US judicial system. Our research uses advanced technologies to develop our understanding of foundational elements of economics and social science, placing our work in the intersection of advanced technology with answering big questions about the world.
The research CAAI does
Our lab applies machine learning techniques to economics, health, social policy, and behavioral science to produce research insights as well as to make a positive difference in the world. The projects are diverse in scope and methods, from observational data to quasi-experimental methods to large-scale field experiments. You can read more about us and our research on the CAAI website.
Here are a few of the problems research professionals have worked on:
The people you’ll work with
You’ll get the benefit of working closely with a faculty member on a project. We also have a community of pre-docs and research professionals that you can learn from. Finally, most of the lab’s projects involve collaboration with external faculty and you will get a chance to learn from them as well.
Here are some researchers we’ve worked with in the past or are currently working with:
- Jon Kleinberg, Cornell University
- Kate Baicker, Harris School of Public Policy
- Oeindrila Dube, Harris School of Public Policy
The candidates we look for
To excel at interdisciplinary work, we require “bilingual” researchers. Firstly, successful researchers are required to master the latest computational tools. Secondly, successful researchers also need to discern the micro-econometric issues that arise with social science data. We are looking for candidates who are strong in one of the above areas, and committed to learning the other. In short, we partly consider this role to be an apprenticeship to learn the trade-craft of research.
Candidates should excel in one of these skills, and want to learn about the others:
In addition, candidates must:
The contributions you’ll make
You’ll further our research program by contributing directly to ongoing research projects. Responsibilities include:
The opportunities you’ll have
As part of your development as a researcher, you will enjoy being part of a community of scholars learning and pursuing research together, as well as:
- Advanced knowledge of data science techniques OR applied micro-econometrics OR theoretical statistics, math, physics or related field.
- Strong initiative and a resourceful approach to problem solving and learning required.
- Ability to work independently and as part of a team in a fast-paced environment required.
- Sound critical thinking skills required.
- Strong attention to detail with superb analytical and organization skills required.
- Excellent written and verbal communication skills, with the ability to present data in a simple and straightforward way for non-technical audiences required.
Education, Experience, and Certifications:
Technical Knowledge or Skills:
- Proficiency with statistical data analysis and machine learning using Python or R is required. The ability to work in both is preferred.
Please apply to our job posting first, and then complete our opening assessment. Applications are currently rolling until July 14, 2022, and involve an assessment process and interview for promising candidates. Our assessment process requires candidates complete a small take-home puzzle set and a data challenge. The fellowship will begin in late Summer/early Autumn.
The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national or ethnic origin, age, status as an individual with a disability, protected veteran status, genetic information, or other protected classes under the law.
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