CDAC Job Openings & Opportunities
We have several positions open for project managers, postdoctoral researchers, and software engineers on CDAC projects and initiatives. Please share these opportunities far and wide across your networks. New openings include:
POSTDOCTORAL SCHOLAR, 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 University of Chicago Center for Data and Computing. 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. Apply
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.