... Strong programming and computational skills • Experience with statistical modeling, machine ... Bayesian methods, probabilistic modeling, or graphical models • Experience with scientific ...
... Strong programming and computational skills • Experience with statistical modeling, machine ... Bayesian methods, probabilistic modeling, or graphical models • Experience with scientific ...
Department of Statistics RESEARCH FELLOW
Ann Arbor, MI · On-site
$65K - $80K/yr
Strong programming and computational skills * Experience with statistical modeling, machine ... Familiarity with Bayesian methods, probabilistic modeling, or graphical models * Experience with ...
Department of Statistics RESEARCH FELLOW
Ann Arbor, MI · On-site
$65K - $80K/yr
Strong programming and computational skills * Experience with statistical modeling, machine ... Familiarity with Bayesian methods, probabilistic modeling, or graphical models * Experience with ...
Probabilistic Programming Bayesian information
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?
| Aspect | Probabilistic Programming Bayesian | Data Scientist |
|---|---|---|
| Required credentials | Background in statistics, probability, programming | Statistics, computer science, or related degree |
| Work environment | Research, modeling, algorithm development | Data analysis, visualization, business insights |
| Industry usage | AI, machine learning, research projects | Business, 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.
What are popular job titles related to Probabilistic Programming Bayesian jobs in Michigan? For Probabilistic Programming Bayesian jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in Michigan look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Michigan are:
What cities in Michigan are hiring for Probabilistic Programming Bayesian jobs? Cities in Michigan with the most Probabilistic Programming Bayesian job openings:
Full-time
Posted 15 days ago
Michigan Medicine rating
7.4
Based on 73 frontline employees who took The Breakroom Quiz
318th of 1,003 rated hospitals
Job description
Job Summary:
Michigan Medicine is seeking a postdoctoral research fellow in statistical genetics and computational genomics within the Terhorst Lab. The role involves developing scalable methods for complex trait analysis and collaborating with researchers across various fields.
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
Qualifications:
Required:
• 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
Preferred:
• 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
Company:
Michigan Medicine is a health care system and academic medical center that provides medical education and more. It is a sub-organization of University of Michigan. Founded in 1869, the company is headquartered in Ann Arbor, USA, with a team of 10001+ employees. The company is currently Late Stage.
Michigan Medicine is seeking a postdoctoral research fellow in statistical genetics and computational genomics within the Terhorst Lab. The role involves developing scalable methods for complex trait analysis and collaborating with researchers across various fields.
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
Qualifications:
Required:
• 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
Preferred:
• 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
Company:
Michigan Medicine is a health care system and academic medical center that provides medical education and more. It is a sub-organization of University of Michigan. Founded in 1869, the company is headquartered in Ann Arbor, USA, with a team of 10001+ employees. The company is currently Late Stage.
What Michigan Medicine employees say
Pay
Benefits
Hours and flexibility
Workplace
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About Michigan Medicine
Sourced by ZipRecruiter
Industry
Health care and social assistance
Company size
10,000+ Employees
Headquarters location
Ann Arbor, MI, US
Year founded
1869