Postdoctoral Research Fellow

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The Tang Lab at the University of Wisconsin–Madison invites applications for a Postdoctoral Research Fellow position in biostatistics, statistics, biomedical data science, and computational omics. The fellow will work with Dr. Zhengzheng Tang and collaborators on methodological and applied research involving multi-omics data, including microbiome/metagenomics, genetics, genomics, and related biomedical data.
The Tang Lab website is available at: https://tangzheng
1.github.io/tanglab/

The University of Wisconsin–Madison is a leading public research university with a strong tradition in statistics, biostatistics, biomedical research, and interdisciplinary collaboration. The campus provides a vibrant research environment, excellent computational resources, and extensive opportunities for collaboration with biomedical scientists, clinicians, and public health researchers.

The postdoctoral fellow will have opportunities to lead and contribute to projects in statistical methodology, machine learning, and biomedical data analysis. Potential research directions include, but are not limited to, microbiome and metagenomic data analysis, multi-omics integration, genetic and genomic studies, causal inference, interpretable machine learning, and methods for complex biomedical data.
Responsibilities may include:

• Conducting independent and collaborative research projects
• Developing statistical and machine-learning methods for biomedical and omics data
• Implementing computational tools and reproducible software
• Designing and conducting simulation studies
• Analyzing real-world biomedical, clinical, and omics datasets
• Interpreting results and preparing manuscripts for peer-reviewed journals
• Collaborating with biologists, clinicians, and healthcare researchers

The position offers strong training in statistical methodology development, software implementation, collaborative biomedical research, and manuscript preparation. The fellow will have opportunities to lead first-author methodological papers and contribute to collaborative publications in high-quality peer-reviewed journals.

Applicants should have, or expect to receive, a PhD in biostatistics, statistics, bioinformatics, computational biology, computer science, data science, or a related quantitative field.

Preferred qualifications include:

• Strong training in statistical methods, machine learning, or quantitative biomedical research
• Proficiency in R, Python, or another programming language
• Experience with simulation studies, real data analysis, or software implementation
• Strong written and oral communication skills
• Ability to work independently and collaboratively
• Interest in solving real-world biomedical and public health problems

Experience with microbiome, metagenomic, genomic, genetic, or other omics data is preferred but not required. Candidates with strong quantitative training and an interest in biomedical applications are encouraged to apply.

The position is available immediately and will remain open until filled. The initial appointment is for one year, with the possibility of renewal for a second and/or third year depending on satisfactory performance and funding availability.