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Causal Inference Machine Learning Postdoctoral Jobs in Humble, TX

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Causal Inference Machine Learning Postdoctoral information

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How much do causal inference machine learning postdoctoral jobs pay per year?

As of Sep 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Humble, TX is $46,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,200.00 and $48,800.00 per year, depending on experience, location, and employer.

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Humble, TX look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Humble, TX are:

What cities near Humble, TX are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Humble, TX with the most Causal Inference Machine Learning Postdoctoral job openings:

Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Humble, TX as of September 2026, with employment types broken down into 89% Full Time, and 11% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $46,822 per year, or $22.5 per hour.

Postdoctoral Fellow - Anesthesiology & PeriOperative Medicine

Houston, TX • On-site, Remote

MD Anderson
Health Care and Social Assistance • 10K+ employees
Learn more about MD Andersonexternal

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Posted 12 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 174 frontline employees who took The Breakroom Quiz

Job description

We are seeking a highly motivated and collaborative Postdoctoral Research Fellow to join our dynamic perioperative research program focused on sleep/ circadian and brain health, delirium and related perioperative neurocognitive disorders in surgical populations. This position offers an exceptional opportunity to lead and contribute to cutting-edge translational research at the intersection of neuroscience, sleep medicine, perioperative care, digital health, and data science.
The successful candidate will spearhead innovative and interdisciplinary research investigating the neurobiological mechanisms underlying perioperative delirium, sleep disturbances, circadian dysfunction, and cognitive impairment in surgical populations. This work integrates large-scale clinical datasets, multimodal physiological measures (e.g., sleep, actigraphy, cognition, mobility/gait, gut microbiome/biomarkers), and advanced computational and statistical approaches to advance our understanding of perioperative brain vulnerability, resilience, and recovery.
This position is ideally suited for candidates with a strong background in neuroscience, sleep and circadian science, aging, cognitive health, nursing, biomechanics, rehabilitation sciences, biomedical engineering, epidemiology, data science, biostatistics, or related fields, with experience conducting hypothesis-driven clinical, translational, or computational research in human populations.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
Data Science & Advanced Analytics (70%)
• Lead complex statistical analyses and predictive modeling of large perioperative datasets using R, SAS, and SPSS
• Develop and implement actigraphy, electroencephalogram protocols and algorithms to process the mentioned variable collected from patients, which includes data cleaning, data analysis and interpretation
• Conduct advanced time-series/longitudinal analyses of sleep and circadian rhythm disruptions in surgical populations
• Perform multivariate analyses integrating clinical, biomarker, and neurocognitive data
• Design and execute computational approaches to analyze neuroimaging and electrophysiological data
• Manage and analyze data collected through REDCap and other clinical databases
• Create data visualizations and interactive dashboards for research dissemination
Neuroscience Research (20%)
• Contribute to mechanistic studies examining the neurobiology of perioperative brain health
• Analyze biomarkers related to neuroinflammation, gut microbiome, neurodegeneration, and synaptic function
• Integrate multi-modal (multi-omics) data (clinical phenotypes, gut microbiome, biomarkers) to elucidate pathways of perioperative brain vulnerability
• Collaborate with neuroscience and translational research teams
Clinical Research Coordination & Administration (10%)
• Coordinate clinical research protocols and ensure regulatory compliance
• Supervise data collection and quality assurance processes
• Mentor junior researchers and research staff
• Lead manuscript preparation and contribute to grant applications
• Present findings at national and international conferences
ELIGIBILITY REQUIREMENTS
• PhD in Neuroscience, Biostatistics, Biomedical engineering, Data Science, Nursing, Epidemiology, or related quantitative field
• Strong programming skills in R (required); proficiency in MATLAB and/or Python, SAS and/or SPSS preferred
• Demonstrated expertise in advanced statistical methods (e.g., mixed-effects modeling, longitudinal analyses, survival analysis, causal inference)
• Track record of peer-reviewed publications in neuroscience, sleep/circadian science, clinical translational research, biomedical engineering or data science
• Strong scientific writing, data visualization, and oral communication skills
• Ability to work independently while collaborating effectively in a multidisciplinary, team-based research environment
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

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