Postdoctoral Fellow – Biomedical Omics Data Science

Statistics Interest Group; Madison (WI); On-site; USD 60,000 - 75,000; Full time

Job summary

The University of Wisconsin–Madison invites applications for a Postdoctoral Research Fellow in biostatistics and interdisciplinary biomedical data science. The position focuses on methodological and applied research with multi-omics data, under Dr. Zhengzheng Tang.

The fellow will develop statistical and machine-learning methods for omics data, implement software, perform simulations, analyze real-world datasets, and prepare manuscripts while collaborating with biologists and clinicians.

Qualifications

• PhD in biostatistics, statistics, bioinformatics, computational biology, computer science, data science, or related quantitative field.
• 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 omics data is preferred but not required.

Responsibilities

• 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

Skills

Statistical methods

Machine learning

Communication skills

Education

PhD in biostatistics/ statistics/ bioinformatics/ computational biology/ computer science/ data science

Tools

R

Python

Job description

The University of Wisconsin–Madison invites applications for a Postdoctoral Research Fellow in biostatistics and interdisciplinary biomedical data science. The position focuses on methodological and applied research with multi-omics data, under Dr. Zhengzheng Tang.

The fellow will develop statistical and machine-learning methods for omics data, implement software, perform simulations, analyze real-world datasets, and prepare manuscripts while collaborating with biologists and clinicians.