POSTDOCTORAL RESEARCHER-HDSB-Xiao Lab-[Req#: Position#:
UT Southwestern Medical Center is looking for POSTDOCTORAL RESEARCHER-HDSB-Xiao Lab-[Req#: Position#: in Dallas, TX.
This local job opportunity with ID 3662849393 is live since 2026-05-04 14:28:34.
Description
A postdoctoral fellow position in AI and Data Science is now available in the laboratories of Dr. Guanghua Xiao at the Quantitative Biomedical Research Center in the Peter O'Donnell School of Public Health at UT Southwestern Medical Center at UT Southwestern Medical Center at Dallas.
The Quantitative Biomedical Research Center (QBRC) is a well-established interdisciplinary research center at UT Southwestern that brings together experts in artificial intelligence, machine learning, predictive modeling, clinical informatics, digital pathology, and biomedical data science. Our goal is to develop cutting-edge computational methods and tools that enable novel discoveries and support data-driven decision-making in health care and public health.
We are seeking highly motivated, creative, and collaborative postdoctoral candidates to join our dynamic team and contribute to a portfolio of research projects applying AI and data science to real-world health care and public health data. The main research areas include:
• Electronic Health Records (EHR)
• Medical imaging (e.g., radiology and pathology)
• Real-time monitoring and wearable sensor data
The successful candidate will have the opportunity to lead and contribute to innovative projects in clinical prediction modeling, disease progression modeling, population health surveillance, and digital biomarker discovery. Our center also supports strong collaborations with clinicians, data scientists, and public health researchers.
Qualifications:
• Ph.D. in Computer Science, Statistics, Biomedical Informatics, Engineering, or a related field.
• Strong programming skills and experience with machine learning, deep learning, or AI applications.
• Interest or experience in working with large-scale health-related datasets.
Qualifications
Qualifications:
• Ph.D. in Computer Science, Statistics, Biomedical Informatics, Engineering, or a related field.
• Strong programming skills and experience with machine learning, deep learning, or AI applications.
• Interest or experience in working with large-scale health-related datasets.
Application Instructions
Application materials must be submitted through Interfolio.
Interested individuals must upload a CV, cover letter, and a list of three references.
Description
A postdoctoral fellow position in AI and Data Science is now available in the laboratories of Dr. Guanghua Xiao at the Quantitative Biomedical Research Center in the Peter O'Donnell School of Public Health at UT Southwestern Medical Center at UT Southwestern Medical Center at Dallas.
The Quantitative Biomedical Research Center (QBRC) is a well-established interdisciplinary research center at UT Southwestern that brings together experts in artificial intelligence, machine learning, predictive modeling, clinical informatics, digital pathology, and biomedical data science. Our goal is to develop cutting-edge computational methods and tools that enable novel discoveries and support data-driven decision-making in health care and public health.
We are seeking highly motivated, creative, and collaborative postdoctoral candidates to join our dynamic team and contribute to a portfolio of research projects applying AI and data science to real-world health care and public health data. The main research areas include:
• Electronic Health Records (EHR)
• Medical imaging (e.g., radiology and pathology)
• Real-time monitoring and wearable sensor data
The successful candidate will have the opportunity to lead and contribute to innovative projects in clinical prediction modeling, disease progression modeling, population health surveillance, and digital biomarker discovery. Our center also supports strong collaborations with clinicians, data scientists, and public health researchers.
Qualifications:
• Ph.D. in Computer Science, Statistics, Biomedical Informatics, Engineering, or a related field.
• Strong programming skills and experience with machine learning, deep learning, or AI applications.
• Interest or experience in working with large-scale health-related datasets.
Qualifications
Qualifications:
• Ph.D. in Computer Science, Statistics, Biomedical Informatics, Engineering, or a related field.
• Strong programming skills and experience with machine learning, deep learning, or AI applications.
• Interest or experience in working with large-scale health-related datasets.
Application Instructions
Application materials must be submitted through Interfolio.
Interested individuals must upload a CV, cover letter, and a list of three references.