Postdoctoral Fellow - Interventional Radiology Research
- Requisition #: 710154-202609030849
- Department: Interventional Rad Rsch
- Location: Houston, TX
- Posted Date: 8/28/2026
Medical Image Analysis, Image-guided therapies, Artificial Intelligence, Data Science
A postdoctoral fellowship position is available in the Department of Interventional Radiology in the laboratory of Dr. Iwan Paolucci and Dr. Bruno Odisio in artificial intelligence and data science for multi-modal tumor response prediction models.
This postdoctoral fellow will engage in highly productive interdisciplinary research projects at the intersection of computational pathology and machine learning. Specific
learning objectives include:
1. Develop proficiency in extracting and analyzing quantitative features from medical images including CT, MRI, PET/CT, and histopathology relevant to tumor characterization and prognosis.
2. Design, implement, and validate machine learning models that integrate multi-modality imaging features with clinical and genomic data for outcome prediction.
3. Evaluate model performance using clinically relevant metrics and validate findings across independent patient cohorts.
4. Translate research findings into scientific communication, including manuscripts, conference presentations, and grant proposals, while collaborating with clinical and research partners across the institution.
The fellow will have opportunities to contribute to ongoing research projects and will be encouraged to explore and develop new areas of research interest with guidance from the mentor. The fellow will be expected to work closely with research/clinical collaborators, communicate findings via reports, abstracts, presentations, and publications, and actively participate in seminars, conferences, and related academic endeavors.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
1. Develop proficiency in extracting and analyzing quantitative features from medical images including CT, MRI, PET/CT, and histopathology relevant to tumor characterization and prognosis.
2. Design, implement, and validate machine learning models that integrate multi-modality imaging features with clinical and genomic data for outcome prediction.
3. Evaluate model performance using clinically relevant metrics and validate findings across independent patient cohorts.
4. Translate research findings into scientific communication, including manuscripts, conference presentations, and grant proposals, while collaborating with clinical and research partners across the institution.
ELIGIBILITY REQUIREMENTS
Applicants should hold a Ph.D. in one of the natural sciences, computer sciences, data science, applied mathematics, engineering, or related fields. Experience with machine learning, deep learning techniques, medical image analysis, or computational modeling is preferred.
ADDITIONAL APPLICATION INFORMATION
Dr. Paolucci is a Biomedical Engineer with a Computer Science background, and his research interests focus includes artificial intelligence and stereotactic and robotic image-guidance for the treatment of hepatobiliary malignancies with a strong focus on thermal ablation of primary and secondary malignant liver tumors. In his research he develops and evaluates algorithms for various aspects of the procedures from patient selection to planning all the way to post-procedure follow-up assessment. Another major focus is on the prediction of oncologic outcome trajectories following loco-regional treatments and treatment recommendation systems using clinical information, imaging and genomics. Applied techniques range from traditional machine learning to deep learning and Bayesian modelling approaches.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
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
A postdoctoral fellowship position is available in the Department of Interventional Radiology in the laboratory of Dr. Iwan Paolucci and Dr. Bruno Odisio in artificial intelligence and data science for multi-modal tumor response prediction models.
This postdoctoral fellow will engage in highly productive interdisciplinary research projects at the intersection of computational pathology and machine learning. Specific
learning objectives include:
1. Develop proficiency in extracting and analyzing quantitative features from medical images including CT, MRI, PET/CT, and histopathology relevant to tumor characterization and prognosis.
2. Design, implement, and validate machine learning models that integrate multi-modality imaging features with clinical and genomic data for outcome prediction.
3. Evaluate model performance using clinically relevant metrics and validate findings across independent patient cohorts.
4. Translate research findings into scientific communication, including manuscripts, conference presentations, and grant proposals, while collaborating with clinical and research partners across the institution.
The fellow will have opportunities to contribute to ongoing research projects and will be encouraged to explore and develop new areas of research interest with guidance from the mentor. The fellow will be expected to work closely with research/clinical collaborators, communicate findings via reports, abstracts, presentations, and publications, and actively participate in seminars, conferences, and related academic endeavors.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
1. Develop proficiency in extracting and analyzing quantitative features from medical images including CT, MRI, PET/CT, and histopathology relevant to tumor characterization and prognosis.
2. Design, implement, and validate machine learning models that integrate multi-modality imaging features with clinical and genomic data for outcome prediction.
3. Evaluate model performance using clinically relevant metrics and validate findings across independent patient cohorts.
4. Translate research findings into scientific communication, including manuscripts, conference presentations, and grant proposals, while collaborating with clinical and research partners across the institution.
ELIGIBILITY REQUIREMENTS
Applicants should hold a Ph.D. in one of the natural sciences, computer sciences, data science, applied mathematics, engineering, or related fields. Experience with machine learning, deep learning techniques, medical image analysis, or computational modeling is preferred.
ADDITIONAL APPLICATION INFORMATION
Dr. Paolucci is a Biomedical Engineer with a Computer Science background, and his research interests focus includes artificial intelligence and stereotactic and robotic image-guidance for the treatment of hepatobiliary malignancies with a strong focus on thermal ablation of primary and secondary malignant liver tumors. In his research he develops and evaluates algorithms for various aspects of the procedures from patient selection to planning all the way to post-procedure follow-up assessment. Another major focus is on the prediction of oncologic outcome trajectories following loco-regional treatments and treatment recommendation systems using clinical information, imaging and genomics. Applied techniques range from traditional machine learning to deep learning and Bayesian modelling approaches.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
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
