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Postdoctoral In Bayesian Statistics Jobs in Missouri

Walmart

Summer 2027 Intern:: Data Science III

Noel, MO · On-site

$23 - $45/hr

... statistical methods and bestpractice advanced modelling techniques eg graphical models Bayesian ... under guidance in solving business issues Data Source Identification Requires knowledge of ...

Walmart

(USA) Data Scientist III

Cassville, MO · On-site

$90K - $180K/yr

Advanced proficiency in statistical methods, machine learning algorithms, and analytical modeling techniques including Bayesian inference and neural networks. * Strong programming skills in SQL ...

Walmart

(USA) Data Scientist III

Noel, MO · On-site

$90K - $180K/yr

Advanced proficiency in statistical methods, machine learning algorithms, and analytical modeling techniques including Bayesian inference and neural networks. * Strong programming skills in SQL ...

Walmart

(USA) Data Scientist III

Anderson, MO · On-site

$90K - $180K/yr

Advanced proficiency in statistical methods, machine learning algorithms, and analytical modeling techniques including Bayesian inference and neural networks. * Strong programming skills in SQL ...

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Postdoctoral In Bayesian Statistics information

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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Cities in Missouri with the most Postdoctoral In Bayesian Statistics job openings:

Postdoctoral In Bayesian Statistics jobs near you
Infographic showing various Postdoctoral In Bayesian Statistics job openings in Missouri as of September 2026, with employment types broken down into 2% Internship, 80% Full Time, 17% Part Time, and 1% Contract. Highlights an 78% Physical, 4% Hybrid, and 18% Remote job distribution.

Physics Modeling Expert - Remote

Saint Louis, MO • Remote

micro1 AI
Software Development • 11 - 50 employees
Learn more about micro1 AIexternal

$100 - $200/hr

Part-time

Re-posted 3 days ago

Job description

Role Title: Physics Expert (PhD / Postdoc)

Role Type: Contractor

Location: Remote (Worldwide)

Schedule: 10-15+ hours a week, Fully Flexible (Can accommodate working after hours/weekends)


micro1 is engaging Physics Experts (PhD / Postdoc) to contribute deep scientific knowledge and problem-solving skills to a high-impact customer project. In this role, you'll apply your expertise to help train next-generation AI systems.


Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Solve advanced physics problems from your specialization, delivering rigorous, well-documented derivations and analyses.
  2. Produce technically precise, clearly written solutions, detailing all assumptions, approximations, and final results using LaTeX mathematical notation.
  3. Utilize SymPy, Python, and Jupyter for symbolic or numerical verification and clear computational workflows where relevant.
  4. Identify and articulate subtleties in problem statements, including special cases, boundary conditions, and dimensional consistency.
  5. Flag ambiguities in project materials, proposing well-reasoned interpretations and clarifications as needed.
  6. Iterate on submitted solutions in response to feedback from project reviewers, ensuring corrections are cleanly integrated.
  7. Uphold rigorous standards in documentation and reproducibility consistent with professional research practice.


Preferred Qualifications

  1. PhD in physics or advanced-stage PhD candidacy, with active research experience in a relevant subfield.
  2. Research expertise in one or more of: High Energy Physics, Mathematical Physics, Biophysics, Statistical Physics, Condensed Matter (including moiré systems, magnetism, PXP/Rydberg), AMO/Quantum Optics, Gravitation, Cosmology, Astrophysics, Quantum Information, or Optical Properties of Materials.
  3. 2–5 recent representative publications (past ~5 years) in your field, with accessible arXiv or DOI records.
  4. Proficiency with LaTeX for presenting mathematics, and with SymPy, Python, and Jupyter for computational work; willingness to indicate areas for further support if needed.
  5. Demonstrated excellence in written technical communication, with a track record of producing clear, precise, and well-argued scientific outputs.
  6. Strong analytical skills, able to isolate key physical principles and provide nuanced solutions to complex problems.
  7. Availability to engage with the project consistently over an 8–10 week period (approx. 10 hours/week).

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