Postdoctoral Scholar, University of Washington. Foy Lab (Computational)
The University of Texas MD Anderson Cancer Center, Seattle, WA, United States
Job Title: Postdoctoral Scholar, University of Washington. Foy Lab (Computational)
Job Number:
127295
Location:
Seattle,US
Job Description
The Foy Lab (foylab dot xyz), at the University of Washington, Department of Laboratory Medicine & Pathology, is hiring a postdoctoral fellow. The postdoctoral scholar will contribute to an exciting research program focused on developing computational methods to enhance how we collect, analyze and use clinical blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data. The chosen candidate will collaborate with clinicians to drive impact from these models, and use them to benefit patient care.
General Duties:
Develop multi-modal statistical and machine learning methods for analysis of health record data for patient diagnosis and outcome prediction.
Perform large-scale querying and analysis of clinical health record databases.
Engage with clinical collaborators, to place the analysis in patient-centered contexts.
Contribute to lab culture, and to dissemination of academic findings.
The ideal candidate will additionally have experience:
Building and validating statistical and machine learning models.
Working with extremely large, multi-modal datasets.
Prior experience in analysis of clinical health records, and time series data are highly preferred.
Application Deadline:
2026-05-15
#J-18808-Ljbffr
Job Number:
127295
Location:
Seattle,US
Job Description
The Foy Lab (foylab dot xyz), at the University of Washington, Department of Laboratory Medicine & Pathology, is hiring a postdoctoral fellow. The postdoctoral scholar will contribute to an exciting research program focused on developing computational methods to enhance how we collect, analyze and use clinical blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data. The chosen candidate will collaborate with clinicians to drive impact from these models, and use them to benefit patient care.
General Duties:
Develop multi-modal statistical and machine learning methods for analysis of health record data for patient diagnosis and outcome prediction.
Perform large-scale querying and analysis of clinical health record databases.
Engage with clinical collaborators, to place the analysis in patient-centered contexts.
Contribute to lab culture, and to dissemination of academic findings.
The ideal candidate will additionally have experience:
Building and validating statistical and machine learning models.
Working with extremely large, multi-modal datasets.
Prior experience in analysis of clinical health records, and time series data are highly preferred.
Application Deadline:
2026-05-15
#J-18808-Ljbffr