Remote-Eligible Postdoc in AI Biostatistics for Health Data

NAACPJobFinder; Ann Arbor (MI); Híbrido; USD 60 000 - 75 000; Tempo integral

Resumo da oferta

The University of Michigan invites applications for a postdoctoral fellow in the Department of Biostatistics to work on statistics, data science, and AI with a focus on biomedical and oncology research. You will develop novel statistical and machine-learning methods under Dr.

Yi Li, collaborating with UM and Harvard investigators and contributing to high-impact publications. The position offers a competitive salary and benefits, with opportunities for teaching and interdisciplinary

Qualificações

• PhD in Biostatistics, Statistics, Mathematics, Computer Science, or a related quantitative field by appointment.
• Strong methodological and computational skills demonstrated in research.
• Interest or experience in health-related applications of statistics, data science, ML, or AI.
• Strong written and oral communication and ability to collaborate in interdisciplinary teams.

Responsabilidades

• Develop innovative statistical and computational methods for high-dimensional biomedical data.
• Apply methods to real-world biomedical and oncology studies and collaborate with investigators.
• Prepare manuscripts for peer‑reviewed journals and present findings.
• Contribute to teaching graduate courses and disseminate research results.
• Work under supervision of Dr. Yi Li and collaborate with UM and partner researchers.

Formação académica

PhD in Biostatistics / Statistics / Mathematics / CS

Descrição da oferta de emprego

The University of Michigan invites applications for a postdoctoral fellow in the Department of Biostatistics to work on statistics, data science, and AI with a focus on biomedical and oncology research. You will develop novel statistical and machine-learning methods under Dr.

Yi Li, collaborating with UM and Harvard investigators and contributing to high-impact publications. The position offers a competitive salary and benefits, with opportunities for teaching and interdisciplinary