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Mathematical Modeling Postdoc Jobs (NOW HIRING)

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX · On-site

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, mathematical modeling, or medical ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, mathematical modeling, or medical ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, mathematical modeling, or medical ...

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Mathematical Modeling Postdoc information

What is the difference between Mathematical Modeling Postdoc vs Data Scientist?

AspectMathematical Modeling PostdocData Scientist
Required CredentialsPhD in Mathematics, Applied Mathematics, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; PhD preferred
Work EnvironmentAcademic research institutions, universities, research labsCorporate, tech companies, startups, or consulting firms
Industry UsageResearch projects, academic publications, grant-funded studiesBusiness analytics, product development, data-driven decision making
Common Search & ComparisonYesYes

While both roles involve analytical skills and data handling, Mathematical Modeling Postdocs focus on academic research and developing theoretical models, whereas Data Scientists apply data analysis techniques to solve practical business problems. The choice depends on whether you prefer research-oriented work or industry applications.

What does a Mathematical Modeling Postdoc do?

A Mathematical Modeling Postdoc conducts advanced research using mathematical techniques to analyze and solve complex real-world problems in fields such as biology, engineering, physics, or social sciences. They typically develop and apply mathematical models, run simulations, analyze data, and interpret results to support scientific or industrial projects. Postdocs in this role often collaborate with interdisciplinary teams, publish research findings, and may also assist in mentoring students or contributing to grant proposals.

What are some common challenges faced by Mathematical Modeling Postdocs when transitioning from academic research to collaborative industry projects?

Mathematical Modeling Postdocs often encounter challenges when moving from academic research to industry settings, particularly in adapting to faster-paced timelines and working within interdisciplinary teams. In industry, projects may require quick prototyping and the ability to communicate complex mathematical concepts to non-experts, such as engineers or business stakeholders. Building effective collaborations and aligning research goals with organizational objectives can also be a significant adjustment. However, these challenges provide valuable experience and broaden career prospects in both academia and industry.

What are the key skills and qualifications needed to thrive as a Mathematical Modeling Postdoc, and why are they important?

A Mathematical Modeling Postdoc requires an advanced degree (typically a PhD) in mathematics, applied mathematics, or a related quantitative field, along with strong analytical and problem-solving abilities. Expertise with programming languages such as Python, MATLAB, or R, and experience with simulation software or computational tools, are commonly expected. Strong communication, collaboration, and critical thinking skills help in presenting findings and working effectively within research teams. These competencies are vital for developing robust models, interpreting complex data, and contributing to innovative research outcomes.
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What states have the most Mathematical Modeling Postdoc jobs? States with the most job openings for Mathematical Modeling Postdoc jobs include:
What job categories do people searching Mathematical Modeling Postdoc jobs look for? The top searched job categories for Mathematical Modeling Postdoc jobs are:
Mathematical Modeling Postdoc jobs near you
Infographic showing various Mathematical Modeling Postdoc job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, 2% Contract, and 1% Nights. Highlights an 94% Physical, and 6% Remote job distribution.
Postdoc Fellow - Imaging Physics

Postdoc Fellow - Imaging Physics

MD Anderson

Houston, TX • On-site

$46K - $63K/yr

Other

Posted 19 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 164 frontline employees who took The Breakroom Quiz

34th of 870 rated healthcare providers

Job description

A postdoctoral fellowship position is available in the Department of Imaging Physics in the laboratory of Chengyue Wu, PhD. Dr. Chengyue Wu's research interests focus on computational precision oncology, especially integrating computational/mathematical approaches with emerging biomedical imaging techniques to improve the diagnosis, prognosis, and treatment of human cancers.

Dr. Wu has extensive experience on developing and validating image processing methods and image-guided models for investigating tumor growth and treatment response, tumor-associated vasculature and microenvironment, and drug delivery. The lab is in a highly collaborative research environment with access to world-class resources, expertise, and data.

Current projects seeking postdoctoral fellows include: Image-guided computational modeling ("digital twins") to predict and optimize cancer (especially breast cancer) treatment response on a patient-specific basis. Development of deep learning models, longitudinal image analysis, and multi-modality data integration to improve breast cancer early detection. LEARNING OBJECTIVES This postdoctoral fellow will engage in highly productive interdisciplinary research projects in image-guided precision oncology and personalized cancer healthcare.

The fellow will expand their knowledge and skills in quantitative imaging, image analysis, artificial intelligence (AI)/deep learning technologies, mathematical biomechanical modeling, inverse problems, and uncertainty quantification. 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.

ELIGIBILITY REQUIREMENTS Applicants should have earned a Ph.D. in one of the natural sciences, computer sciences, applied mathematics, engineering, or related fields or a medical degree. Experience with machine learning and deep learning techniques, mathematical modeling, or medical image analysis is preferred

Applicants do not need to be US citizens or permanent residents. This appointment is not part of a clinical training program; individuals holding an M.D. degree or equivalent are not permitted to engage in patient care activity

ADDITIONAL APPLICATION INFORMATION The trainee will be appointed for one year from the date of hire with an option to be renewed for up to three years. POSITION INFORMATION 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 FACULTY MENTOR Dr

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