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Postdoctoral In Bayesian Statistics Jobs in Massachusetts

Chewy

Machine Learning Engineer II

Boston, MA · On-site

$161K/yr

Master's degree in Industrial Engineering, Operations Research, Statistics, Applied Mathematics, or ... ML techniques like neural networks, linear regression, Bayesian statistics and XG boost models.

Chewy

Machine Learning Engineer II

Boston, MA · On-site +1

$161K/yr

Master's degree in Industrial Engineering, Operations Research, Statistics, Applied Mathematics, or ... ML techniques like neural networks, linear regression, Bayesian statistics and XG boost models.

Costello Medical

Statistician Intern

Boston, MA · On-site +1

$54K - $66K/yr

... Bayesian network meta-analysis using both standard and emerging methods. This internship is ideal for both graduates and current students who are looking to gain experience in the field of statistics ...

Costello Medical

Statistician Intern

Boston, MA · On-site

$54K - $66K/yr

... Bayesian network meta-analysis using both standard and emerging methods. This internship is ideal for both graduates and current students who are looking to gain experience in the field of statistics ...

Costello Medical

Statistician Intern

Boston, MA · On-site

$54K - $66K/yr

... Bayesian network meta-analysis using both standard and emerging methods. This internship is ideal for both graduates and current students who are looking to gain experience in the field of statistics ...

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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
Postdoctoral Research Fellow in Statistical Machine Learning and Biomedical AI

Postdoctoral Research Fellow in Statistical Machine Learning and Biomedical AI

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Posted 25 days ago

Harvard University rating

8.1

Company rating: 8.1 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

130th of 537 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral Research Fellow in Statistical Machine Learning and Biomedical AI
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
Position Description
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Research Fellow position in statistics, genetics, and biomedical AI. The lab develops cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and computational challenges arising in precision medicine, mental health, and biomedical informatics.
The postdoctoral fellow will contribute to projects focused on:
  • Foundation and representation learning for multimodal biomedical data, including electronic health records (EHRs), genomics, imaging, and clinical text to power next-generation precision medicine.
  • Statistical and computational genomics across diverse populations and biobanks for risk prediction, genetic discovery, and genomic medicine.
  • Federated and transfer learning for distributed and privacy-preserving data integration.
  • AI and Deep learning approaches to high-dimensional and multi-modal biomedical data.
  • Causal Inference, Fairness, and Trustworthy AI in real-world healthcare applications.

Our group actively collaborates with large national and international initiatives, including Mass General Brigham, Penn Medicine, Cambridge Health Alliance, PsycheMERGE Network, PCORnet, and OHDSI, providing unique opportunities to work with massive EHR-genomic datasets and multi-site real-world evidence networks.
Basic Qualifications
  • Ph.D. in Statistics, Biostatistics, Computer Science, Statistical Genetics, or a related quantitative field (by the time of appointment).
  • Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis.
  • Proficiency in R or Python; experience with deep learning, causal inference, or genetic data analysis is not required but encouraged.
  • Excellent written and verbal communication skills.

Additional Qualifications
Special Instructions
The position is available immediately. The initial appointment is for one year, renewable based on performance and funding. Salary and benefits follow NIH and Harvard guidelines.
Interested applicants should submit a CV, cover letter, and contact information for three references to Dr. Rui Duan (rduan@hsph.harvard.edu). Review of applications will begin immediately and continue until the position is filled.
Contact Information
Rui Duan, Associate Professor, Department of Biostatistics
Contact Email
rduan@hsph.harvard.edu
Salary Range
$75,000
Minimum Number of References Required
3
Maximum Number of References Allowed
Keywords
biostatistics; AI; biomedical informatics; statistics; genetics

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