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Postdoctoral Research Scientist in Biostatistics
Company Information
The Department of Biostatistics at Columbia University in the City of New York is seeking 1-2 Postdoctoral Research Scientists (Postdocs) with a focus on statistical learning and biomedical data science to work with Dr. Tian Gu and Dr. Ying Wei. These positions provide exciting opportunities to engage in cutting-edge AI-driven research with applications in cancer and dementia risk prediction. Postdocs will also benefit from collaboration with the Translational AI Laboratory (TRAIL) in Columbia Biostatistics and the Columbia Data Science Institute, fostering interdisciplinary research at the forefront of biostatistics, machine learning, and translational biomedical science.
Focus: Statistical Learning and Biomedical Data Science
The Postdoctoral Research Scientists will focus on developing innovative statistical and machine-learning methods for integrating data and evidence from various types of complex biomedical data (e.g., electronic health record data, claims data, and biobanks). Areas of particular interest include but are not limited to: (1) genetic risk prediction for underrepresented populations; (2) distributed statistical inference in clinical research networks; (3) statistical methods for high-dimensional data. Specific responsibilities include:
•Advancing AI-driven statistical and machine learning methods for multi-source data integration.
•Developing and implementing scalable computational solutions for analyzing complex biobank data linked to electronic health records.
•Translating research findings into impactful applications in cancer risk prediction and other areas of precision medicine.
•Collaborating with researchers at TRAIL and the Columbia Data Science Institute to leverage interdisciplinary expertise.
•Contributing to high-impact publications in both methodological and applied journals.
Candidates must hold a doctoral degree in statistics, biostatistics, bioinformatics, or a related field. Preferred qualifications include:
•Strong training in statistical learning, AI methods, and computational tools.
•Experience with large-scale biomedical data analysis, particularly in genomic or EHR contexts.
•Proficiency in programming languages such as R, Python, or C++.
•Excellent written and verbal communication skills.
•A demonstrated ability to work independently and collaboratively within interdisciplinary teams.