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Open Positions

I am looking for multiple highly-motivated students and postdocs to work together on exciting research projects.
Welcome to contact me if you are interested.

Current PhD/MSc/MPH students: GRA positions for PhD students are available immediately. RA positions for MSc/MPH students depend on qualifications; please email me with your research interests and CV.

Prospective PhD students: our department has a PhD admission committee to handle PhD student applications. You are very welcome to put my name in your PhD application. You may email me as well to let me know what you are interested in and how I can help.

Postdoc Position 1: Postdoctoral Research Scientist Position in Computational Genomics

Job Title: Postdoctoral Research Scientist.

Department: Department of Biostatistics at Columbia University.

A Postdoctoral Research Scientist position is now open in Dr. Wenpin Hou’s research group within the Department of Biostatistics at Columbia University. Our team is at the forefront of developing innovative computational and statistical methods to analyze intricate data types in genomics and data science. This includes areas such as single-cell genomics, epigenomics, and spatial transcriptomics. Our ultimate aim is to deepen our understanding of gene regulation and enhance human health. We seek candidates who possess a solid foundation in deep learning and transformers, computational genomics, network modeling, functional data analysis, spatiotemporal data modeling, time series analysis, or related domains. Those eager to apply their expertise to address pressing challenges in genomics and health are particularly encouraged to apply. The selected individual will collaborate closely with Dr. Hou and esteemed colleagues from Columbia University, Johns Hopkins University, and New York University. They will be instrumental in devising and implementing state-of-the-art methods on expansive datasets from sources like NIH-funded initiatives and major consortium projects. This role offers the chance to spearhead and co-author influential publications, showcase findings at renowned conferences, and acquire invaluable experience in statistical machine learning as applied to single-cell genomics, multi-omics, and spatial transcriptomics. Additionally, the candidate will benefit from mentorship in career progression and grant writing, including guidance on securing awards like the NIH K99/R00.

Availability and Renewal: The position is available immediately and is renewable annually based on performance and funding availability.

Salary and Benefit: Salary is competitive and commensurate with qualification. Benefits will be in accordance with Columbia University postdoctoral benefits.

Required Qualifications: Applicants should have a PhD degree or equivalent in computer science, biostatistics, statistics, mathematics, biomedical engineering, or related fields. Applicants should also have excellent programming skills (e.g., R, Python) and communication skills.

How to Apply: Please send a cover letter stating your research interests and qualifications, a CV, and contact information of three references to Dr. Wenpin Hou at wh2526@cumc.columbia.edu. Please use “Postdoc Application” as the email subject.

Postdoc Position 2: Post-doc Research Associate on Deep Learning in Medical Imaging, Computational Genomics, and Multimodal Data Analysis (joint with Dr. Ying Wei)

The Department of Biostatistics at Columbia University invites applications for a prestigious postdoctoral position at the intersection of deep learning, computational genomics, and multimodal data analysis. This role encompasses two exciting areas:

• Medical Image Analysis, Electronic Medical Records, and Multimodal Data Integration: The selected candidate will delve into cutting-edge research projects aimed at revolutionizing our understanding of various medical conditions and developing innovative preventative interventions and care approaches.
• Understanding the Mechanistic Basis of the Human Genome: This facet of the position involves developing novel computational methods to elucidate gene regulation and its circuitry, dynamics, response to therapy, variability across populations, and applications across diverse fields, including cancer and immunotherapy, neuroscience, inflammatory processes, and evolution. Special attention will be given to the integration of multi-omics data to characterize time-varying developmental trajectories in tissue-spatial landscapes and to categorize spatial heterogeneity in gene regulation.

Key Responsibilities:

• Develop and implement advanced deep learning algorithms for medical image analysis, with a special focus on algorithms integrating mammographic images for precision breast cancer screening.
• Develop and implement advanced methods (deep learning, functional data analysis, Bayesian models, and network modeling) to characterize gene regulation across multiple samples and in spatially separated tissue regions using single-cell multi-omics and spatial genomics data.
• Devise and implement state-of-the-art methods on expansive datasets from sources like NIH-funded initiatives and major consortium projects.
• Collaborate in the integration and analysis of multimodal data sources, combining imaging data, biobank, electronic medical records, and multi-omics data.
• Contribute to the publication of research findings in high-impact journals and conferences.
• Collaborate closely with a multidisciplinary team, including epidemiologist, clinicians, data scientists, engineers, biologists, and physicians.
• Mentor graduate and undergraduate students in the research group.

How to Apply: Please send a cover letter stating your research interests and qualifications, a CV, and contact information of three references to Dr. Wenpin Hou at wh2526@cumc.columbia.edu. Please use “Postdoc Application” as the email subject.