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Postdoctoral In Bayesian Statistics Jobs (NOW HIRING)

MD Anderson

Postdoctoral Fellow - Biostatistics

Houston, TX · On-site +1

$64K - $76K/yr

The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...

MD Anderson

Postdoctoral Fellow - Biostatistics

Houston, TX · On-site +1

$64K - $76K/yr

The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...

MD Anderson

Postdoctoral Fellow - Biostatistics

Houston, TX · On-site +1

$64K - $76K/yr

The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...

MORSE Corp

Bayesian Data Scientist

Cambridge, MA · On-site

$90K - $210K/yr

MORSE is searching for an experienced Data Scientist with expertise in Bayesian statistics, probabilistic modeling, data analysis, data science, and algorithm development in one or more of a variety ...

Think Tank, Inc.

Statistician

Seattle, WA · On-site

Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred.Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience ...

Think Tank, Inc.

Statistician

Seattle, WA

Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred. * Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience ...

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How much do postdoctoral in bayesian statistics jobs pay per year?

As of Aug 24, 2026, the average yearly pay for postdoctoral in bayesian statistics in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is a postdoctoral position in Bayesian statistics?

A Postdoctoral position in Bayesian Statistics is a research-focused role for individuals who have recently completed their PhD in statistics, mathematics, or a related field. These positions involve conducting advanced research using Bayesian methods, which apply probability to infer statistical conclusions. Postdocs often work on developing new Bayesian models, collaborating on interdisciplinary projects, and publishing research findings. Such positions are typically temporary and designed to further prepare researchers for academic, industry, or governmental roles.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in Bayesian statistics?

To thrive as a Postdoctoral Researcher in Bayesian Statistics, you need an advanced degree (typically a PhD) in statistics or a related field, with strong expertise in Bayesian inference and probabilistic modeling. Proficiency with statistical programming languages such as R, Python, or Stan, and experience with specialized Bayesian analysis software are highly valued. Excellent problem-solving skills, collaboration, and the ability to communicate complex statistical concepts clearly are standout soft skills for this role. These skills and qualities are crucial for conducting rigorous research, publishing impactful results, and contributing effectively to scientific teams.

What are some common challenges faced by postdoctoral researchers in Bayesian statistics, and how can they be addressed?

Postdoctoral researchers in Bayesian statistics often encounter challenges such as managing complex, high-dimensional data, staying current with rapidly evolving computational methods, and balancing independent research with collaborative projects. Effective strategies include leveraging open-source statistical software, actively participating in seminars and workshops to stay updated, and establishing regular communication with interdisciplinary teams. Building a strong professional network and seeking mentorship within the department can also help in navigating research obstacles and advancing one's career.

What is the difference between Postdoctoral In Bayesian Statistics vs Postdoctoral In Data Science?

AspectPostdoctoral In Bayesian StatisticsPostdoctoral In Data Science
Required CredentialsPhD in Statistics, Mathematics, or related fieldPhD in Computer Science, Statistics, or related field
Work EnvironmentAcademic research, university labsResearch institutions, tech companies, industry labs
Employer & Industry UsageUniversities, research institutesTech firms, finance, healthcare, consulting
Common Search & Comparison IntentSpecialized research roles in Bayesian methodsBroader data analysis and machine learning roles

Postdoctoral In Bayesian Statistics focuses on advanced research in Bayesian methods within academic settings, requiring deep statistical expertise. In contrast, Postdoctoral In Data Science covers a broader range of data analysis techniques, including machine learning, often in industry environments. Both roles require a PhD but differ in application focus and work environment.

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Postdoctoral In Bayesian Statistics jobs near you
Infographic showing various Postdoctoral In Bayesian Statistics job openings in the United States as of August 2026, with employment types broken down into 79% Full Time, 19% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

POSTDOCTORAL RESEARCHER-Bayesian Biostatistics for Multicenter Clinical Trials-DeSantis Lab [Req#: 9

UT Southwestern Medical Center

Dallas, TX • On-site

Full-time

Posted 17 days ago

UT Southwestern rating

7.9

Company rating: 7.9 out of 10

Based on 152 frontline employees who took The Breakroom Quiz

108th of 893 rated healthcare providers

Job description

Description
POSTDOCTORAL RESEARCHER
UTSW invites applications for a Postdoctoral Fellow in Bayesian Biostatistics. The fellow will contribute to both applied and methodological research involving Bayesian methods for multicenter clinical trials, with a particular focus on phase III studies in trauma, trauma resuscitation, and traumatic brain injury.
This position is suited to a recent PhD graduate who is interested in developing a research program at the intersection of Bayesian methodology and collaborative clinical-trial research. The fellow will work closely with biostatisticians, clinical investigators, trial leadership, and multidisciplinary research teams involved in large, multicenter studies.
Research activities
The fellow's work will include:
  • Conducting statistical analyses of multicenter clinical-trial data.

  • Developing statistical analysis plans, including specification of estimands, prior distributions, analysis populations, missing-data methods, interim analyses, and sensitivity analyses.

  • Contributing to the design, conduct, monitoring, and reporting of phase III clinical trials.

  • Developing and evaluating Bayesian methods relevant to multicenter trials.

  • Conducting simulation studies to assess type I error, power, bias, interval coverage, operating characteristics, and robustness to prior-data conflict.

  • Preparing manuscripts, abstracts, presentations, technical reports, and grant applications.

  • Collaborating with clinicians and investigators in trauma, trauma resuscitation, and traumatic brain injury research.

Methodological projects may include, but are not limited to:
  • Bayesian borrowing of historical or external information.

  • Dynamic and robust borrowing methods.

  • Hierarchical models for multicenter studies.

  • Commensurate, mixture, power, and meta-analytic predictive priors.

  • Bayesian approaches to phase III confirmatory trials.

  • Bayesian interim monitoring and adaptive trial methods.

  • Modeling treatment-effect heterogeneity across centers or patient subgroups.

  • Methods for ordinal, longitudinal, survival, and competing-risk outcomes.

  • Sensitivity analyses for prior specification, missing data, and departures from modeling assumptions.

The fellow will have opportunities to lead methodological and applied manuscripts, present findings at national scientific meetings, and participate in the development of grant proposals and future clinical-trial protocols.
Appointment and mentorship
The fellow will receive mentorship in:
  • Bayesian methodology development.

  • Statistical leadership in multicenter clinical trials.

  • Collaborative biostatistics.

  • Manuscript and grant preparation.

  • Development of an independent research program.

The initial appointment will be for one year.
Qualifications
Required qualifications
Applicants should have:
  • A PhD in biostatistics, statistics, or a closely related quantitative field, completed recently-typically within the past one to two years-or expected before the position start date.

  • Formal training in Bayesian statistical methods.

  • Strong programming skills in R.

  • Experience with statistical simulation and reproducible data analysis.

  • A strong foundation in statistical inference, regression modeling, and clinical-trial methodology.

  • The ability to communicate statistical concepts effectively to both quantitative and clinical collaborators.

  • Strong scientific writing and organizational skills.

  • An interest in conducting both applied and methodological research.

Preferred qualifications
Preferred experience includes one or more of the following:
  • Bayesian hierarchical modeling or Bayesian borrowing methods.

  • Clinical-trial design or analysis.

  • Development of statistical analysis plans.

  • Multicenter or cluster-structured data.

  • Stan, RStan, cmdstanr, JAGS, NIMBLE, or other Bayesian computing platforms.

  • Longitudinal, ordinal, time-to-event, or competing-risk methods.

  • Analysis of trauma, emergency medicine, critical care, or neurological outcomes.

  • Preparation of peer-reviewed manuscripts or collaborative grant proposals.

Prior experience in trauma research is not required. Applicants with strong Bayesian training and an interest in clinical-trial applications are encouraged to apply.
Application Instructions
Interested individuals should send a CV and a list of three references to:
Stacia DeSantis
Stacia.Desantis@UTSouthwestern.edu

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