Postdoctoral Researchers
DSI postdoctoral researchers are the next generation of leaders in data science.
Postdocs engage in independent and novel research in an environment that fosters multidisciplinary, collaborative work.
The Data Science Institute is actively seeking recent Ph.D. graduates and doctoral candidates that expect to receive their degree by July 1, 2023 who have explicit interests in advancing and/or applying data science to other domains.
Prior experience in data science is not required. We look for candidates with the courage and interest to learn a new field, to be part of a prestigious cohort of like-minded fellows with the goal of helping to define the field of data science into the future.
Columbia University has a rich and diverse group of mentors whom our postdoctoral fellows collaborate. As part of the application process, you will identify potential mentors with whom you would like to work. Please find a list of DSI affiliated faculty members here; and see below for a list of collaborators that may be relevant to your application.
Open Applications
Apply to 2023-2024 Postdoctoral Research Programs:
Data Science Institute Postdoctoral Research Scientist Application
Apply to Specialized Postdoctoral Research Positions:
Data Science Institute Data and Cancer Collaboratory Post-Doctoral Scientist
Postdoctoral Research Scientist (Powered by the Mastercard Center for Inclusive Growth)
Postdoctoral Research Scientist (Research Collaboration with the Natural Language Processing Group)
Application Process
Mentor Selection
- Candidates should identify between two and five potential mentors who are current Columbia faculty members, including DSI faculty affiliates. Candidates are welcome to look beyond this list
- If you are interested in applying data science to a specific domain, we encourage you to identify mentors from both a foundational area (applied mathematics, computer science, electrical engineering, machine learning, optimization, statistics, etc.) and a domain area. Using your input, the DSI will ensure the best possible match of mentor(s) for you
Dates and Deadlines
- Application Deadline: December 15, 2022. Candidates are encouraged to apply as early as possible. There may be limited space for postdoctoral researchers beyond this deadline, and the DSI may consider applications from exceptional candidates. If you would like more information, please reach out to Ming Yuan, Associate Director for Academic Affairs.
- Appointments Begin: July 1, 2023 (Cohort 5)
Funding
DSI Postdoctoral Fellows are guaranteed funding for two years through year-long renewable appointments. In addition to a competitive salary package, DSI Postdoctoral Fellows will also be provided with a research budget to be used for conference travel, computing equipment, and/or other research-related costs.
In collaboration with other schools, centers, and institutes at Columbia, the Data Science Institute also encourages candidates with specific interests in the following areas. Please indicate the area(s) within your cover letter.
Statement on Racial Equity
The Data Science Institute is committed to racial equity and justice. Research statements should explicitly state that the project will uphold these values, e.g., stating that the methods used to collect and analyze project data and the project outcomes reported are fair, just, and ethical.
Required Application Materials
- A cover letter that explains your motivation for applying to this program and indicates your choice of mentors (and special interest area if applicable)
- A curriculum vitae, including a list of publications
- A brief research statement that summarizes current research interests, past accomplishments, and future research goals. It should contain a short proposal for the research activities you plan to do as a DSI Fellow, including a schedule with milestones, and statement and commitment to racial equity
- Up to three representative research papers
- Three letters of reference submitted directly by the recommenders
[Optional] A letter from a potential Columbia faculty mentor indicating an interest in your candidacy and your proposed research activities. This letter can be submitted directly to dsi-fellows@columbia.edu

Short List of Potential Columbia University Collaborations
We welcome applications for postdoctoral researchers interested in any of these collaborations. If you are interested in one of these opportunities, please note it in your cover letter.
- Learning the Earth with Artificial Intelligence and Physics (LEAP) seeks fellows who will contribute to our mission of developing novel machine learning algorithms to harness data from high-fidelity simulations or sparse and noisy observations to improve climate projections. We are particularly interested in candidates who use physical and causal constraints or Bayesian approaches.
- The Herbert and Florence Irving Institute for Cancer Dynamics seeks fellows who will support our shared mission of improving the understanding of cancer biology, origins, treatment, and prevention through data-driven approaches. We are particularly interested in candidates who will further advance core research in statistics, machine learning, AI and probabilistic modeling.
- The Zuckerman Mind Brain Behavior Institute seeks fellows who are interested in using advanced data science and computational methods to understand how the brain works, from the neuron level to the cognitive level.
- The Columbia Climate School seeks fellows who are interested in the interdisciplinary study of the science, consequences, and human dimensions of climate change. We are particularly interested in the urgent problems of Coastal Resilience, Global Decarbonization, Disaster Resilience, and Food for Humanity. We invite postdoctoral contributions in the basic and translational sciences, as well as in applied policy and direct societal interventions.
- The Neuro Technology Center seeks fellows who will assist in our shared mission to support the development of advanced optical, electrical, and computational technologies for the study of complex neurobiological systems using data-driven methods. We are particularly interested in candidates who will further advance core research in computational neuroscience and its links with machine learning and artificial intelligence.
- The Mailman School of Public Health seeks fellows who will assist in our shared mission to support and amplify health analytics. We are particularly interested in candidates who will further advance core research in the use of advanced data analytics to solve issues pertaining to population-scale health issues and preventive strategies, e.g., modeling climate effects on health, integrative-omics in large populations, impact of social networks on health, and data-streaming from personal monitoring devices.
- The Center on Poverty and Social Policy seeks fellows who will support our shared mission to address the role of social policy in reducing poverty. We are particularly interested in candidates who will further advance core research in understanding trends in historical poverty, economic insecurity, immobility, and inequality, using novel data sources, and data-driven methods. Specifically we will further advance the usage of data that utilizes scanned receipts and optical recognition software to try to assess the impacts of the recently-enacted expansion of the Child Tax Credit on spending behavior.
- The Department of Psychiatry seeks fellows interested in applying data science and cognitive systems to research on mental health, substance use disorders, and neuroscience. Opportunities for scalable data-driven science in psychiatry and clinical neuroscience are growing rapidly, and fellows would help advance the state-of-art in these areas. We are particularly interested in areas such as utilizing large and real-time medical record systems for addressing optimal patient-clinician feedback and treatment decisions; and utilizing multimodal neuroimaging data (including large consortia like the adolescent brain cognitive development [ABCD] study) for discovering underlying brain mechanism and development of psychiatric disorders.
- The Department of Statistics seeks fellows who will support our shared mission of developing statistical insights into machine learning in the context of emerging challenges such as domain adaptation, representation learning, differential privacy, fairness, causal inference, etc. We are particularly interested in candidates who will expand core statistical methods and theory, as motivated by cutting-edge data science applications with broad societal impacts. Successful candidates are expected to engage and bridge between core faculty from Statistics, and faculty in any of the many departments involved with the Columbia data science center.
Postdoctoral Researchers
Daniel Alabi
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Data Science Institute
Postdoctoral Research Scientist
Meghan Bucher
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Mailman School of Public Health
Postdoctoral Research Fellow in the Department of Environmental Health Sciences
Christian Dye
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Mailman School of Public Health
Postdoctoral Research Fellow in the Department of Environmental Health Sciences
Amir Feder
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Data Science Institute
Postdoctoral Research Scientist
Ann Iturra Mena
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Data Science Institute
Postdoctoral Research Scientist
Abraham Liddell
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Data Science Institute
Postdoctoral Research Scholar
Kelton Minor
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Data Science Institute
Postdoctoral Research Scientist
Gemma Moran
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Data Science Institute
Postdoctoral Research Scientist
Orestis Papadigenopoulos
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Data Science Institute
Postdoctoral Research Scientist
Wei Tang
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Data Science Institute
Postdoctoral Research Scientist
Eli Weinstein
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Data Science Institute
Postdoctoral Research Scientist
Postdoctoral Researchers, Past and Present
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The Data Science Institute postdoctoral program welcomes recent Ph.D. graduates from diverse disciplinary backgrounds. The following researchers have participated in the program since its inception in 2017.
Arpit Agrawal, University of Pennsylvania, Computer and Information Science
Christian Andersson Naesseth, Linköping University (Sweden), Electrical Engineering
Brielin Brown, University of California, Berkeley, Computer Science
Megan Bucher, University of Pittsburgh, Neuroscience
Craig Connolly, The University of Texas at Austin, Marine Science
Kyle Davis, University of Virginia, Environmental Sciences
Caitlin Dreisbach, University of Virginia, Data Science
Christian Ka’ikekūponoaloha Dye, University of Hawaiʻi at Mānoa, Molecular Biosciences and Engineering
Ipek Ensari, University of Illinois Urbana-Champaign, Kinesiology and Exercise Science
Kira Goldner, University of Washington, Computer Science and Engineering
Yinqiu He, University of Michigan, Statistics
Aviv Landau, University of Haifa (Israel), Social Work
Abraham Liddell, Vanderbilt University, History
Jackson Loper, Brown University, Applied Mathematics
Debmalya Mandal, Harvard University, Computer Science
Andrew Miller, Harvard University, Computer Science
Gemma Moran, University of Pennsylvania, Statistics
Sandrine Müller, Cambridge University (United Kingdom), Psychology
Annie Nigra, Columbia University, Environmental Health Sciences
Yevgeny Rakita Shlafstein, Weizmann Institute of Science (Israel), Materials and Interfaces
Adele Ribeiro, University of São Paulo (Brazil), Computer Science
Alexander Root, Cornell University, Systems Biology
Aaron Schein, University of Massachusetts Amherst, Computational Social Science
Andrew Sonta, Stanford University, Civil Engineering
Miranda Spratlen, Johns Hopkins University, Environmental Health and Engineering
Emily L. Spratt, Princeton University, Art History, Historic Preservation, Computer Vision Science
Dhanya Sridhar, University of California, Santa Cruz, Computer Science
Chistopher Tosh, University of California, San Diego, Computer Science
Rami Vanguri, University of Pennsylvania, Elementary Particle Physics
Mingzhang Yin, Princeton University, Statistics
Isabelle Zaugg, American University, Communication