Research Intern - Radiation Oncology (Dr. Enderling Lab)
- Requisition #: 600661-202505211637
- Department: Radiation Oncology - Research
- Location: Houston, TX
- Posted Date: 5/20/2025
The goal of this unique research program is to develop predictive models of radiation therapy response using routinely collected biopsy tissue samples from cancer patients. By leveraging cutting-edge machine learning (ML) and artificial intelligence (AI) techniques, we aim to derive predictive and prognostic signatures of treatment response and outcomes. This integrative approach combines computational modeling with real-world clinical data to enhance precision oncology efforts in radiation therapy. Research interests in the Enderling lab includes image-guided radiation therapy, computational approaches to improve treatment planning, digital twins, and integration of novel research developments with clinical applications.
This project offers a unique opportunity for trainees interested in the intersection of computational biology, oncology, and data science, preparing them for future careers in research, clinical informatics, or applied machine learning in healthcare.
LEARNING OBJECTIVES
Participants in this program will gain practical, interdisciplinary experience in the following areas:
• Clinical Data Abstraction: Learn to extract and curate relevant patient data from EPIC (electronic health record system), including demographics, treatment details, outcomes, and pathology reports.
• Clinical Data Analysis: Gain hands-on experience performing descriptive and statistical analyses on structured and unstructured clinical datasets to identify patterns associated with radiation response.
• Algorithm Development & Deployment: Apply and adapt ML/AI models to clinical datasets to develop predictive tools that can be validated and tested for clinical utility.
• Translational Research Integration: Understand how computational findings can inform clinical decision-making and treatment planning in radiation oncology.
ELIGIBILITY REQUIREMENTS
Applicant must hold a bachelor's or master's degree;
Degree must have been obtained recently (within one year)
Applicant must have previous research experience;
Applicants who hold a Ph.D. or equivalent doctoral degree (e.g., M.D., D.V.M., M.B.B.S.) are not eligible;
Applicants associated with a home institution do not qualify;
Applicants in a clinical position, postdoctoral fellowship, or faculty position do not qualify;
Research Interns cannot change titles to another trainee title upon completion.
Exception applies if the trainee enrolls in a degree-seeking program
The appointee trains under the supervision of a faculty member/mentor
POSITION INFORMATION
12/31/2025
This project offers a unique opportunity for trainees interested in the intersection of computational biology, oncology, and data science, preparing them for future careers in research, clinical informatics, or applied machine learning in healthcare.
LEARNING OBJECTIVES
Participants in this program will gain practical, interdisciplinary experience in the following areas:
• Clinical Data Abstraction: Learn to extract and curate relevant patient data from EPIC (electronic health record system), including demographics, treatment details, outcomes, and pathology reports.
• Clinical Data Analysis: Gain hands-on experience performing descriptive and statistical analyses on structured and unstructured clinical datasets to identify patterns associated with radiation response.
• Algorithm Development & Deployment: Apply and adapt ML/AI models to clinical datasets to develop predictive tools that can be validated and tested for clinical utility.
• Translational Research Integration: Understand how computational findings can inform clinical decision-making and treatment planning in radiation oncology.
ELIGIBILITY REQUIREMENTS
Applicant must hold a bachelor's or master's degree;
Degree must have been obtained recently (within one year)
Applicant must have previous research experience;
Applicants who hold a Ph.D. or equivalent doctoral degree (e.g., M.D., D.V.M., M.B.B.S.) are not eligible;
Applicants associated with a home institution do not qualify;
Applicants in a clinical position, postdoctoral fellowship, or faculty position do not qualify;
Research Interns cannot change titles to another trainee title upon completion.
Exception applies if the trainee enrolls in a degree-seeking program
The appointee trains under the supervision of a faculty member/mentor
POSITION INFORMATION
