Postdoctoral Scholar, Portland, Oregon

Oregon Health & Science University

Oregon Health & Science University values a diverse and culturally competent workforce. We are proud of our commitment to being an equal opportunity, affirmative action organization that does not discriminate against applicants on the basis of any protected class status, including disability status and protected veteran status. Individuals with diverse backgrounds and those who promote diversity and a culture of inclusion are encouraged to apply. To request reasonable accommodation contact the Affirmative Action and Equal Opportunity Department at 503-494-5148 or aaeo@ohsu.edu.


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.

Select references:

• https://www.nature.com/articles/s41467-021-27615-8
• https://www.eu-focus.europeanurology.com/article/S2405-4569(23)00077-9/fulltext
• https://www.biorxiv.org/content/10.1101/2023.08.18.553854v1

Responsibilities

Education & experience:

• D., M.D./Ph.D., or Ph.D.
• Minimum of 3 years as a graduate student

Knowledge, skills, and abilities:

• Excellent communication, analytical and organizational skills
• Ability to work independently and as part of a team while being collaborative in resolving problems
• Passion for science and inquiry
• Ability to synthesize one’s own results and others to formulate hypotheses
• Energy and drive to coordinate multiple projects simultaneously

Qualifications

• PhD with major coursework in the one of the following areas: computational biology, statistics, computer science or machine learning
• Programming experience with Python
• Background in one and more of following topics: image computing, deep learning, dimensionality reduction
• Experience in developing computational approaches for modeling imaging data
• Ability to clearly communicate and collaborate with researchers from diverse backgrounds, including wet-lab scientists

PI238738995