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Postdoctoral In Bayesian Statistics Jobs in Dallas, TX

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Statistics Graduate Level Tutor

Plano, TX · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

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Statistics Graduate Level Tutor

Irving, TX · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

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Statistics Graduate Level Tutor

Dallas, TX · Remote

$18 - $40/hr

... In a Statistics Graduate Level Tutor * Advanced Subject Mastery: Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian ...

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

As of Jul 4, 2026, the average yearly pay for postdoctoral in bayesian statistics in Dallas, TX is $58,386.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,500.00 and $65,800.00 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 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.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Bayesian Statistics, and why are they important?

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.
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Postdoctoral In Bayesian Statistics jobs near you
POSTDOCTORAL RESEARCHER - Epidemiology & Health Data Science and Biostatistics - Luan Lab [Req#: 934

POSTDOCTORAL RESEARCHER - Epidemiology & Health Data Science and Biostatistics - Luan Lab [Req#: 934

UT Southwestern Medical Center

Dallas, TX • On-site

Full-time

Posted 14 days ago

UT Southwestern rating

7.8

Company rating: 7.8 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

104th of 877 rated healthcare providers

Job description

Description
POSTDOCTORAL RESEARCHER - We invite applications for a Postdoctoral Researcher position focused on applying LLMs and GeoAI in geospatial health data analysis. This position will involve training and fine-tuning LLMs and GeoAI models to analyze geospatial health datasets on different topics (e.g., HIV, cardiology, and cancer) from various data sources (e.g., longitudinal health surveys, Electronic Health Record, open data). The successful candidate will also help develop an LLM-based spatial analysis tool, promote its use by public health researchers and practitioners, and assess the tool's acceptability and feasibility.
Description of Duties and Responsibilities:
• Train and fine-turning LLMs and GeoAI models for geospatial health data analysis.
• Develop an LLM-based spatial analysis tool and assess its acceptability and feasibility.
• Collaborate with experts in epidemiology, health data science, and computer science.
• Prepare manuscripts for peer-reviewed journals.
• Present research at academic conferences.
• Assist and develop new grant proposals on related topics for submission to funding agencies.
• Mentor graduate students in the research team.
Term: One year with potential extension based on performance and funding availability
Visa sponorship: J-1 if needed
Qualifications
Required Qualifications:
• A Ph.D. in Computer Science, Health Informatics, Geospatial Data Science, Statistics, or a related field.
• Strong background in (geospatial) AI and generative AI/LLM.
• Strong R or Python programming skills.
• Proven ability to publish in high-impact academic journals.
• Strong written and oral communication skills.
Desired Qualifications
• Experience with GeoAI applications in Street View Images and Remote Sensing
• Knowledge of (Bayesian) spatial statistics, spatial accessibility, and spatial optimization
• Knowledge of spatial epidemiology
Application Instructions
Interested individuals must upload a CV, cover letter, and a list of three references.

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