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Postdoctoral Scholar
US-OR-Portland
Job ID: 2024-29020 Type: Regular Full-Time # of Openings: 1 Category: Postdoctoral Portland, OR (Waterfront)
Overview
CEDAR funds its own research projects, expediting the process of discovery. Our research is milestone-driven to ensure that each project is fulfilling its stated goals. CEDAR offers a unique opportunity for outstanding, driven, and creative postdoctoral fellows to perform cutting edge and high-risk research, ranging from understanding basic cancer biology to developing novel technologies to aid detection. Our focus on early detection requires novel approaches to reliably identify small/rare signals in diverse data types, with an emphasis on minimally invasive sampling techniques.
We are currently hiring for a highly motivated fellow with expertise in Computational Biology to work in the following areas. Candidate would primarily work with Dr. Ece Eksi, Assistant Professor in the Division of Oncological Sciences with affiliations in the Biomedical Engineering and Cell, Development and Cancer Biology departments.
Image computing – Performing analysis of existing imaging data sets of prostate tumor samples using established pipelines and developing novel machine learning approaches. The successful candidate will have the opportunity to analyze single-cell, multiplex imaging data sets and develop image computing pipelines to aid clinical decision-making processes in cancer diagnostics.
Machine Learning/Statistics – Developing machine learning methods and spatial statistical models to study the organization of the cell types in the tumor microenvironment using cutting-edge spatial imaging datasets. Developed algorithms will be used to build predictive temporal models for patient stratification. Techniques such as spatial auto-correlation, dimensionality reduction and clustering will be utilized.
Single-cell transcriptomics and epigenomics - Modeling and analyzing next-generation sequencing datasets that measure the transcriptome and the epigenome, specifically single-cell RNA-seq and ATAC-seq. The successful candidate will use both existing tools and develop their own computational methods to analyze such datasets for the characterization of prostate and pancreatic tumors.