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Causal Inference Machine Learning Postdoctoral Jobs in Humble, TX

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$124K - $167K/yr

Advanced proficiency with complex queries, window functions, and optimization • Machine Learning ... Strong foundation in statistics, A/B testing, causal inference, and experimental design • ...

New

Hexagon AB

Lead Data Scientist

Houston, TX · On-site

Advanced proficiency with complex queries, window functions, and optimization Machine Learning ... Strong foundation in statistics, A/B testing, causal inference, and experimental design ...

MD Anderson

Postdoctoral 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, statistical modeling, biomechanical ...

MD Anderson

Postdoctoral 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, statistical modeling, biomechanical ...

Cisco

Principal Machine Learning Engineer

Houston, TX · On-site

$291K - $369K/yr

... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ... Learning - Fluency in automated retraining, drift detection, incremental updates, and production ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Humble, TX

Causal Inference Machine Learning Postdoctoral information

See Humble, TX salary details

$30.7K

$46.8K

$52.7K

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

As of Jul 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, and why are they important?

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.

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:
Causal Inference Machine Learning Postdoctoral jobs near you
Postdoctoral Associate - Aortic Imaging & Clinical Research

Postdoctoral Associate - Aortic Imaging & Clinical Research

Baylor College of Medicine

Houston, TX

Full-time

Posted 26 days ago

Baylor College of Medicine rating

8.6

Company rating: 8.6 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

57th of 555 rated colleges and universities

Job description

Summary

A Postdoctoral Associate position is available in the Division of Vascular Surgery’s Vascular Intelligence for Translational AI & Learning (VITAL) Lab, Michael E. DeBakey Department of Surgery. The Postdoctoral Associate will focus on clinical data collection, data management, and imaging-based and clinical research in aortic disease. The Postdoctoral Associate must hold an MD or MD/PhD degree with clinical research experience, or a PhD degree in biomedical engineering, medical physics, computational science, or a related field with experience in vascular imaging research. 

The Postdoctoral Associate will receive training under the guidance of the Principal Investigators and collaborating investigators to conduct prospective or retrospective imaging-based and clinical research projects focused on aortic disease and complex endovascular aortic repairs. Responsibilities include protocol development, study design, data collection and analysis, interpretation of clinical and imaging findings, manuscript preparation, and presentation of research findings at scientific meetings. This position provides an excellent platform for career development and the opportunity to contribute to cutting-edge research in advanced endovascular aortic therapies within a collaborative, multidisciplinary research environment. 

Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.

Job Duties
  • Participates in prospective and retrospective imaging-based and clinical research projects focused on aortic disease and complex endovascular aortic repair, including protocol development, study design, clinical data collection and management, and imaging data organization and analysis.
  • Participates in vascular imaging research that includes CTA/DICOM image acquisition, interpretation, imaging-based outcome analysis, and AI- and digital twin-based research initiatives.
  • Develops research protocols that includes IRB submissions and amendments.
  • Contributes to coordinate multicenter clinical research studies.
  • Works collaboratively and professionally within multidisciplinary teams. 
  • Performs data analysis and interpretation of clinical and imaging research findings.
  • Prepares high-quality abstracts, manuscripts, presentations, scientific reports, and other academic materials for peer-reviewed publications and local, national, and international professional conferences.
  • Contributes to scholarly and collaborative research activities.
  • Completes assigned responsibilities accurately and efficiently.
  • Maintains research documentation and data quality standards.
  • Perform other job-duties as assigned.
Minimum Qualifications
  • MD or Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.
Preferred Qualifications
  • MD or MD/PhD degree with clinical research experience, or a PhD degree in biomedical engineering, medical physics, computational science, or a related field with experience in vascular imaging research. 
  • Experience in clinical or imaging-based research.
  • Familiarity with vascular imaging software, image post-processing, and AI/machine learning applications in clinical or imaging research is desirable.
  • Demonstrates strong written and verbal communication skills, attention to detail, professionalism, and the ability to manage multiple research responsibilities in a timely and organized manner.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.

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