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What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

What are the key skills and qualifications needed to thrive as a Probabilistic Programming Bayesian specialist, and why are they important?

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
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Probabilistic Programming Bayesian jobs near you
Department of Statistics RESEARCH FELLOW

Department of Statistics RESEARCH FELLOW

University of Michigan

Ann Arbor, MI • On-site

$65K - $80K/yr

Full-time

Posted 25 days ago

University Of Michigan rating

8.1

Company rating: 8.1 out of 10

Based on 140 frontline employees who took The Breakroom Quiz

131st of 532 rated colleges and universities

Job description

How to Apply
To apply, please submit the following materials via email to: [email protected]
  • A cover letter summarizing research experience and interests
  • Curriculum vitae, including publications and/or preprints
  • Contact information for three references

Applications will be reviewed on a rolling basis until the position is filled.
Job Summary
The Terhorst Lab in the Department of Statistics at the University of Michigan is recruiting a postdoctoral research fellow in statistical genetics and computational genomics. The position is part of a collaborative project with the University of Edinburgh and the University of Oxford focused on developing scalable methods for complex trait analysis using ancestral recombination graphs (ARGs).
Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed models, and efficient algorithms for large-scale genomic data analysis.
This is a one-year appointment starting as early as possible, with renewal possible based on the availability of funds, availability of work, and satisfactory performance.
Responsibilities*
  • Develop novel statistical and computational methods for ARG-based quantitative genetics
  • Analyze large-scale genetic and phenotypic datasets
  • Implement scalable software and algorithms for genomic inference
  • Collaborate with researchers across statistics, genetics, and computational biology
  • Contribute to manuscripts, presentations, and open-source software development
  • Participate in interdisciplinary collaborations related to predictive breeding and genome editing
  • Travel to the United Kingdom to collaborate with project partners at the University of Edinburgh and the University of Oxford

Required Qualifications*
  • PhD in statistics, computer science, computational biology, genetics, applied mathematics, or a related quantitative field is required
  • Proof of degree completion must be in hand on or before the start date
  • Strong programming and computational skills
  • Experience with statistical modeling, machine learning, or large-scale data analysis

Desired Qualifications*
  • Experience with population genetics or statistical genetics
  • Familiarity with Bayesian methods, probabilistic modeling, or graphical models
  • Experience with scientific computing in Python, JAX, Torch, Julia, C++, or related languages
  • Experience with high-performance computing or scalable algorithms
  • Interest in interdisciplinary research spanning genomics and evolutionary biology

Modes of Work
The position is on-site in Ann Arbor, Michigan.
Additional Information
Salary and Benefits: Salary will be commensurate with experience and is expected to be in the range of $65,000 - $80,000 per year, plus benefits and a $3,000 relocation assistance bonus.
Background Screening
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third-party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.
Contact Information
Please direct questions about the position to Jonathan Terhorst at [email protected] .
Please direct questions about how to apply to: [email protected]
U-M EEO Statement
The University of Michigan is an equal employment opportunity employer.
Job Detail
Job Opening ID
277543
Working Title
Department of Statistics RESEARCH FELLOW
Job Title
RESEARCH FELLOW
Work Location
Ann Arbor Campus
Ann Arbor, MI
Modes of Work
Onsite
Full/Part Time
Full-Time
Regular/Temporary
Regular
FLSA Status
Exempt
Organizational Group
College Of Lsa
Department
LSA Statistics
Posting Begin/End Date
5/12/2026 - 6/13/2026
Salary
$65,000.00 - $80,000.00
Career Interest
Research Fellows

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About University of Michigan

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The University of Michigan (U-M), based in Ann Arbor, MI, US, is one of America's most esteemed institutions in higher education. Established in 1817, it presides in the industry of education and research, providing a range of services including undergraduate, graduate, and professional education programs. Complementing this is an extensive research activity that has significantly contributed to various fields, from healthcare to engineering, humanities to sports. Upholding its mission "to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values", U-M consistently ranks among the top universities globally, a testament to its tradition of excellence in learning and research, and a deep commitment to innovation and discovery.

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Company size

10,000+ Employees

Headquarters location

Ann Arbor, MI, US

Year founded

1817

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