Research Investigator - Radiation Oncology - Research

The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees and the public.
The primary purpose of the Research Investigator is to assist the Principal Investigator and other Faculty members with scientific research projects. Learns and adapts techniques for use in ongoing research, plans and assists training and research work activity for research personnel, and provides guidance and training to early-stage researchers within the lab. Supervises use of laboratory equipment, implements safety procedures, maintains records, compiles, writes and submits project results to superiors. Participates in planning and preparation of applications for grants and other research support pipelines. Reads and understands current scientific literature.
The Research Investigator will execute research projects under the supervision of Dr. Joseph D. Butner, a computational and data scientist in the Radiation Oncology department at MD Anderson Cancer Center, with a focus on developing skills to transition the trainee from mentored research to independent research. Research Topics include mathematical and computational modeling of cancer development and therapy for predicting therapeutic response and improving patient outcomes. Primary duties will include development and coding of mechanistic computational models of systemic and targeted therapy, statistical models, and deep learning models, authorship of peer-reviewed publications and grant applications, conference attendance to disseminate results to the scientific community, assisting the principal investigator with mentoring junior lab members, and leading collaborative projects between the research team, clinical staff, and industry partners. Successful applicants will demonstrate a successful track-record of applying predictive models to measured data and effective presentation of results. Candidates with extensive data science experience are especially encouraged to apply.
This position is within a clinical department, providing a rare opportunity for computational researchers to directly interface with clinicians on a day-to-day basis to pursue improved cancer outcomes through engineered, personalized treatment strategies. The selected candidate will play an integral role in helping to spearhead the early stages of long-term research conducted in the Division of Radiation Oncology and at the newly developed Institute for Data Science in Oncology (IDSO) at MD Anderson Cancer Center (MDACC), will have the chance to establish their own systems and have a major impact in shaping the lab's culture, and will influence the lab's approach to science for the coming years. While working with both clinical collaborators in the Radiation Oncology department and other computational scientists in IDSO at MDACC, the postdoctoral fellow will gain valuable experience in designing predictive tools that are readily deployable to current clinical practice.
Candidates should have an interest in adapting computational and mathematical modeling approaches to integrate within the limitations of real-world clinical operations, and in overcoming the challenges that often restrict computational models to theory-generation only. Relevant skills include strong mathematical competency and rapid comprehension of new statistical methods, and experience in object-oriented programming including Python or R, with proficiency in C++ preferred. They should also be knowledgeable in building software in cluster computing (e.g., bash, cmake, working in the terminal), including compiling against linking third-party libraries in Linux environments.
KEY FUNCTIONS
1. AUTONOMOUSLY PERFORM MODEL DESIGN, DEVELOPMENT AND DEPLOYMENT. INSTRUCTS AND ASSISTS OTHER LAB MEMBERS ON MODEL DEVELOPMENT AS NEEDED.
2. PARTICIPATE IN THE DEVELOPMENT OF PYTHON LIBRARIES FOR SCALABLE DEPLOYMENT OF DEEP-LEARNING MODELS TO PREDICT PATIENT OUTCOMES TO SUPPORT ONGOING AND FUTURE PROJECTS.
3. PERFORM RIGOROUS STATISTICAL ANALYSIS AND VERIFICATION OF MODEL OUTPUTS AND PREDICTIONS.
4. WORK WITH CLINICIANS, RESIDENTS, AND OTHER MODELERS TO DEVELOP AND USE STATISTICAL AND DEEP-LEARNING MODELS TO GUIDE TARGETED RADIATION THERAPY BY IDENTIFYING LESIONS LIKELY TO ACHIEVE THERAPEUTIC RESPONSE.
5. WORK ALONGSIDE IDSO LEADERSHIP TO ESTABLISH ROBUST DATA PIPELINES FOR RAPID THROUGHPUT OF DATA INTO PREDICTIVE MODELING PLATFORMS.
6. TRAINING AND SUPERVISION OF OTHER RESEARCH PERSONNEL ON TECHNIQUES AND COMPUTATIONAL AND MATHEMATICAL APPROACHES. CRITICALLY REVIEWS RESEARCH CONDUCTED BY OTHER LAB MEMBERS TO VERIFY SCIENTIFIC INTEGRITY AND PROVIDE CRITICAL FEEDBACK.
7. ASSIST IN WRITING OF SCIENTIFIC PAPERS FOR PUBLICATION, GRANT APPLICATIONS AND PRESENTATIONS AT SCIENTIFIC MEETINGS
8. OTHER DUTIES AS ASSIGNED
WORKING CONDITIONS
This position requires:
Working in Office Environment ______ No ___X___ Yes
Working in Patient Care Unit (e.g. Nursing unit; outpatient clinic) X No ______ Yes
Exposure to human/animal blood, body fluids, or tissues ______ No X Yes
Exposure to harmful chemicals ______ No X Yes
Exposure to radiation ______ No X Yes
Exposure to animals ______ No X Yes
PHYSICAL DEMANDS
Indicate the time required to do each of the following physical demands:
Time Spent
Never
0% Occasionally
1-33% Frequently
34-66% Continuously
67-100%
Standing X
Walking X
Sitting X
Reaching X
Lifting/Carrying
Up to 10 lbs X
10lbs to 50 lbs X
More than 50 lbs X
Pushing/Pulling
Up to 10 lbs X
10lbs to 50 lbs X
More than 50 lbs X
Use computer/keyboard X
Education
Required: Bachelor's degree in one of the natural sciences or related field.
Experience
Required: Five years of experience in scientific or experimental research work. With preferred degree, three years of required experience.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html