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Professor of Biomedical Data Science (Assistant, Associate, and/or Professor Level)

Oregon Health and Science University - Knight Cancer Institute
Portland, Oregon
Commensurate with education, experience, and internal equity
Closing date
Apr 22, 2024

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Life Sciences, Bioinformatics, Computational Biology, Other, Other
Position Type
Full Time
Job Type
Organization Type

The Center for Biomedical Data Science (CBDS) at the Knight Cancer Institute (KCI) at Oregon Health and Science University (OHSU) is searching for multiple tenured or tenure-track faculty positions at all ranks (Assistant Professor, Associate Professor, and/or Professor) in the area of Biomedical Data Science.  Faculty will be members of CBDS with primary tenure home in the Division of Oncological Sciences (DOS) at KCI. A secondary appointment in a different unit at OHSU is possible depending on the candidate's discipline and research focus.

The newly founded CBDS, as a nexus for research in data science at OHSU, is dedicated to advancing precision health in general, and precision early cancer detection and therapies in particular, by analyzing large, complex, multi-modal multi-scale data through the development and implementation of novel computational analytical tools and methods. The data science goals include identifying biomarkers associated with response and resistance to therapy; modeling etiologic causes of cancer; modeling the development and progression of cancer; organizing and processing large data resources to create harmonized datasets; and mining datasets for novel subtypes of diseases. The field analyzes all kinds of cancer data, including omics, anatomical imaging to molecular imaging to nanoscale imaging, cellular and pathway interactions, electronic medical records, and published literature. A key area of focus is integrating datasets, such as combining public and private datasets or merging data across modalities, and doing novel analyses on integrated datasets.

To serve these goals, CBDS seeks interdisciplinary scholars whose work spans diverse areas, including but not limited to: machine learning, computer vision, natural language processing, AI-enabled health, computational biology, systems biology, bioinformatics, biostatistics, dynamic modeling, responsible and interpretable AI, multi-scale multi-modal data integration, and data analytics and visualization.

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