Summary: 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.
Background: 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.
Mentorship: Postdoctoral scholars will be co-mentored by a subset of the following PIs, depending on the fellow’s interests, skills, and specific project.
● Samantha Riesenfeld, Pritzker School of Molecular Engineering, Dept. of Medicine (genomics-based data science, systems immunology)
● Alexander Pearson, Dept. of Medicine (clinical oncology, deep learning in digital pathology)
● Michael Maire, Dept. of Computer Science (computer vision)
● Greg Shakhnarovich, Toyota Technological Institute at Chicago, Dept. of Computer Science (computer vision, machine learning)
● Robert Grossman, Dept. of Medicine, Dept. of Computer Science (translational data science)
Our team, led by Prof. Riesenfeld, deeply values and promotes an inclusive, multicultural environment that supports diverse scientists in learning, leading, creating, and collaborating.
Qualifications: Candidates should have or expect a PhD in Computer Science, Computational Biology, Statistics, or a related field, with substantial research experience in at least one of the relevant subfields, such as neural network approaches to image analysis or statistical analysis of functional genomic data. Candidates will ideally have strong track records of performing research both independently and collaboratively.
To Apply: Please email your CV and a cover letter to:
Tagged as: Life Sciences
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