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Postdoctoral In Bayesian Statistics Jobs in Massachusetts

Atavistik Bio

Sr. Director, Biostatistics

Cambridge, MA · On-site

$270K - $290K/yr

Advanced knowledge of statistical methods in clinical study designs (adaptive, Bayesian), statistical analysis methods including Bayesian method, missing data imputation, multiplicity adjustment

Atavistik Bio

Sr. Director, Biostatistics

Cambridge, MA · On-site

$270K - $290K/yr

Advanced knowledge of statistical methods in clinical study designs (adaptive, Bayesian), statistical analysis methods including Bayesian method, missing data imputation, multiplicity adjustment

Varsity Tutors

Statistics Graduate Level Tutor

Newton, MA · 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 ...

Varsity Tutors

Statistics Graduate Level Tutor

Waltham, MA · 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 ...

UMass Med School

Post Doc - Open Rank

Worcester, MA · On-site

$75K/yr

Additional Information Postdoc in Causal Inference of Complex Gene Networks We invite applications ... Position Overview You will design, implement, and apply new computational and statistical models to ...

Varsity Tutors

Statistics Graduate Level Tutor

Boston, MA · 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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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 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
Post Doc - Open Rank

Post Doc - Open Rank

University of Massachusetts Medical School

Worcester, MA

$62K - $75K/yr

Full-time

Posted 10 days ago

Job description

Postdoc in Causal Inference of Complex Gene Networks

We invite applications for a NIH-funded postdoctoral researcher position in our computational lab at UMass Chan Medical School. We develop methods to reconstruct multi-modal causal networks that govern cellular behavior from large-scale single-cell datasets. Our group has pioneered computational approaches for:

  • Inferring causal networks from Perturb-seq (interventional single-cell CRISPR screens).
  • Mapping dynamic network rewiring from joint scRNA-seq + scATAC-seq.
  • Identifying state-specific causal networks from population-scale scRNA-seq.

We approach single-cell biology as a high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative skills and curiosity about complex systems.

Position Overview

You will design, implement, and apply new computational and statistical models to reverse-engineer causal networks from noisy, high-dimensional, multi-modal data. This role offers high independence, rapid idea testing, and close collaboration with an interdisciplinary team.

If you are excited about tackling problems in complex networks, causal inference, and high-dimensional systems, and applying them to understand how molecular interactions drive cell states and transitions, this is an excellent fit.

Key Responsibilities
  • Develop accurate and scalable algorithms for inferring multi-modal, condition-dependent networks from datasets with millions of samples (cells) between tens of thousands of nodes (genes and genetic features).
  • Apply these algorithms on existing and new datasets to uncover biological principles and insights across molecular, cellular, and population levels.
  • Build open-source, user-friendly software tools for the community.
  • Disseminate findings through peer-reviewed publications, user-friendly software packages, and academic presentations.
  • Collaborate with other group members and research groups as needed.

Apply