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Machine Learning Postdoc Jobs in Houston, TX (NOW HIRING)

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Machine Learning Postdoc information

What is a machine learning postdoc?

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?

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 key skills and qualifications needed to thrive in a machine learning postdoc position?

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 are the most commonly searched types of Machine Learning Postdoc jobs in Houston, TX?

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For Machine Learning Postdoc jobs in Houston, TX, the most frequently searched job titles are:

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Cities near Houston, TX with the most Machine Learning Postdoc job openings:

Machine Learning Postdoc jobs near you
Infographic showing various Machine Learning Postdoc job openings in Houston, TX as of August 2026, with employment types broken down into 92% Full Time, 4% Part Time, and 4% Contract. Highlights an 96% In-person, and 4% Hybrid job distribution.

Postdoctoral Fellow - Breast Surgical Oncology - Research

MD Anderson Cancer Center

Houston, TX • On-site

$46K - $63K/yr

Full-time

Re-posted 14 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 171 frontline employees who took The Breakroom Quiz

14th of 887 rated healthcare providers

Job description

The postdoctoral fellow will work on an externally funded methods development project focused on causal inference for long-term pharmacotherapy. The central methodological contribution is a framework for "patient-choice (PC) protocols": a class of treatment strategies that allow patients to flexibly balance quality of life and clinical outcomes during sustained pharmacotherapy. The applied context is adjuvant endocrine therapy for patients with breast cancer. The fellow will contribute to theoretical development, estimation, and real-world application of these methods across large healthcare claims databases (Merative MarketScan, SEER-Medicare) and data from an international phase-III clinical trial (PALLAS).
Under the general guidance of the Principal Investigator, the postdoctoral fellow will:
• Develop and formalize causal estimands using counterfactual theory, causal directed acyclic graphs, and single world intervention graphs.
• Derive efficient influence functions and develop targeted minimum loss-based estimation (TMLE) algorithms for a general class of PC protocols, including extensions to dynamic regime marginal structural models.
• Implement doubly robust, semiparametrically efficient estimators that incorporate flexible machine learning algorithms for confounding control in high-dimensional longitudinal data.
• Develop and apply sensitivity analysis methods.
• Analyze large-scale observational claims data and contribute to open-source software development in R.
• Prepare manuscripts and present at national and international conferences.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
• The fellow will develop expertise in a novel class of causal estimands for longitudinal treatment strategies with non-adherence, building from foundational identification theory through to efficient nonparametric estimation and open-source implementation.
• The fellow will gain hands-on experience applying causal inference methods in large-scale healthcare claims data.
• The postdoctoral fellow will participate in a weekly causal inference conference with opportunities to collaborate on methods development and applied projects in causal inference research.
• The fellow will develop an independent publication record through authorship on methods and applied manuscripts, and will have opportunities to present work and lead workshops at major statistical and epidemiologic conferences.
ELIGIBILITY REQUIREMENTS
• Ph.D. in Biostatistics, Statistics, or Epidemiology with a strong focus on causal inference methods
• Strong programming skills in R and SAS required, with experience using large administrative claims datasets
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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