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Machine Learning Postdoc Jobs in Texas (NOW HIRING)

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

Postdoctoral Fellow - Biostatistics

Houston, TX · On-site

$64K - $76K/yr

Some experience with machine learning and AI is desirable. Please send CV and information on three referees directly to zli16@mdanderson.org. POSITION INFORMATION MD Anderson offers full-time postdoc ...

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Machine Learning Postdoc information

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$19.3K

$100.3K

$188.6K

How much do machine learning postdoc jobs pay per year?

As of Jul 7, 2026, the average yearly pay for machine learning postdoc in Texas is $100,252.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,007.00 and $138,071.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Postdoc position, and why are they important?

To thrive as a Machine Learning Postdoc, you need a deep understanding of machine learning algorithms, statistical modeling, and research methodology, typically supported by a completed PhD in a related field. Proficiency with programming languages like Python or R, experience with ML libraries (e.g., TensorFlow or PyTorch), and familiarity with large-scale datasets and cloud computing platforms are important. Strong analytical thinking, effective communication, and the ability to collaborate across multidisciplinary teams are standout soft skills in this position. These qualifications ensure innovative research contributions, successful project execution, and effective dissemination of findings in both academic and applied settings.

What is a Machine Learning Postdoc job?

A Machine Learning Postdoc is a research-focused position typically held after earning a Ph.D. in a related field. It involves conducting advanced research in machine learning, developing new algorithms, and publishing in top-tier conferences and journals. Postdocs often collaborate with faculty, industry partners, and other researchers to advance the state of the art in AI. The role may include mentoring students and contributing to grant proposals. It serves as a bridge between doctoral studies and a long-term academic or industry research career.

What are the typical responsibilities and collaborative aspects of a Machine Learning Postdoc position?

A Machine Learning Postdoc typically conducts original research, develops and tests new algorithms, and contributes to academic publications or patent applications. Daily tasks often involve data analysis, model building, and experimentation using advanced computational tools. Collaboration is key in this role, as postdocs frequently work alongside faculty, graduate students, and external industry partners to advance research objectives. Additionally, they may mentor junior researchers or students, present at conferences, and participate in grant writing or project planning. This mix of independent research and team collaboration fosters both professional growth and impactful scientific advancements.

What are the most commonly searched types of Machine Learning Postdoc jobs in Texas? The most popular types of Machine Learning Postdoc jobs in Texas are:
What are popular job titles related to Machine Learning Postdoc jobs in Texas? For Machine Learning Postdoc jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Machine Learning Postdoc jobs in Texas look for? The top searched job categories for Machine Learning Postdoc jobs in Texas are:
What cities in Texas are hiring for Machine Learning Postdoc jobs? Cities in Texas with the most Machine Learning Postdoc job openings:
Machine Learning Postdoc jobs near you
Infographic showing various Machine Learning Postdoc job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $100,252 per year, or $48.2 per hour.
Postdoc Fellow - Imaging Physics

Postdoc Fellow - Imaging Physics

MD Anderson

Houston, TX

$46K - $63K/yr

Full-time

Posted 15 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 168 frontline employees who took The Breakroom Quiz

32nd of 877 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


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Dr. Chengyue Wu

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