Postdoctoral Scholar, Data & Democracy Research Initiative
The University of Chicago is seeking Postdoctoral Scholars focused on the intersection of democracy and data science. The postdoctoral scholar will be part of the new Data & Democracy research initiative, a major interdisciplinary collaboration jointly led by the University of Chicago Data Science Institute (DSI) and the Center for Effective Government (CEG).
The Data & Democracy research initiative is a unique collaboration between computer scientists, statisticians and political scientists to better understand democracy in the digital age. This initiative will investigate critical questions concerning the impact of misinformation on effective government, how online communication translates into offline political behavior, and the implications of the consolidation of online media platforms for free speech. We invite researchers to join this initiative to spearhead new interdisciplinary research projects as part of a growing community of scholars.
Postdoctoral Scholar on Artificial Intelligence Alignment
We’re seeking postdoctoral researchers to work on Artificial Intelligence alignment: the problem of ensuring that future AI systems will be beneficial. We are particularly interested in developing foundational understanding of how large pretrained models (e.g., large language models) represent and manipulate concepts and world knowledge. The researchers will be advised by (some combination of) prof. Victor Veitch, prof. David McAllester, and/or prof. Chenhao Tan, depending on postdoc preference and fit. Candidates should have a strong interest in representation learning, natural language understanding, explainability, or modern deep learning. Candidates should have a PhD in machine learning, computer science, statistics, or a similar field.
Contact firstname.lastname@example.org if interested. Applications will be reviewed on a rolling basis, with an ideal start date in early 2023.
Postdoctoral Scholar in Machine Learning Foundations
The University of Chicago is seeking candidates for postdoctoral research on machine learning foundations. Postdoctoral scholars will lead interdisciplinary research projects and collaborations in machine learning methods that incorporate physical models and constraints, providing additional robustness and reduced sample complexity. Scholars will create new methods that draw upon methods and insights from multiple fields, including inverse problems, data assimilation, optimization, and statistics, and explore applications in climate science, biomedical imaging, and remote sensing. In addition to competitive salary and benefits, the fellowship also includes funding for independent travel to workshops, conferences and other universities and research labs. Interested applicants should contact Rebecca Willett with a CV, research statement, and letter of reference.
Postdoctoral Scholar in Cancer-Focused Machine Learning
The University of Chicago Pritzker School of Molecular Engineering is seeking candidates for 2–3 postdoctoral scholar research positions, funded primarily by the UChicago Data Science Institute. Postdoctoral scholars will lead interdisciplinary research projects and collaborations in the areas of machine learning, genomics, clinical cancer care, and image-based computer vision. Advances in genomics have led to new cancer therapies that target specific genetic or molecular features, raising the potential for effective personalized treatments with reduced side effects. However, the majority of patients treated with targeted therapies do not respond as predicted, and detailed patient genomic information is expensive to acquire. The goals of this initiative are to develop new artificial intelligence approaches that improve targeting of cancer treatment by combining multiple streams of genetic information with tumor pathology images. Postdoctoral scholars will create new methods that draw upon computer vision and machine learning to extract essential contextual information about individual cancers from tumor samples, utilizing genomic, transcriptional, and image-based features.
Postdoctoral Scholars & Graduate Students, Deep Learning in Cosmology and Astrophysics
We are seeking collaborators for a short-term project on the development and application of statistical and deep learning techniques for the analysis of Cosmic Microwave Background (CMB) imaging data. We are seeking postdocs and graduate students with experience in statistics and deep learning applications for computer vision in the context of astronomy and cosmology. The scope of work includes the innovation, development, and application of deep learning techniques for the analysis of CMB. Both projects will likely touch on issues and opportunities to advance deep learning algorithms, methods in uncertainty quantification for machine learning, and high-performance computing. We are seeking to collaborate as soon as possible, and aim to complete work (including submit publications) by late 2022. We can provide funding for graduate students or a postdoc for about a year.
For more information, contact Brian Nord.
Postdoctoral Scholar, Mapping & Mitigating the Urban Digital Divide
The University of Chicago is seeking a Postdoctoral Scholar focused on internet performance and measurement to work closely with Nick Feamster, Neubauer Professor of Computer Science and Faculty Director of Research at the UChicago Data Science Institute. The Urban Digital Divide project is a major interdisciplinary research initiative, funded by Data.org, focused on addressing the issue of Internet access by using large-scale Internet measurement and data science to help communities and stakeholders to better assess the quality and accessibility of broadband Internet access.
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