Postdoctoral Researchers
DSI postdoctoral researchers are the next generation of leaders in data science.
Postdocs carry out independent and novel research in an environment that fosters multidisciplinary, collaborative work.
- We are actively seeking candidates who are recent Ph.D. graduates and 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 select like-minded cohort of fellows, and to help define the field of data science for the long-term future.
We encourage applications from candidates with diverse backgrounds, experiences, and identities.
Click here to apply for DSI’s digital identity postdoctoral research scientist role.
Click here to apply for DSI’s data and cancer postdoctoral research scientist role.
Click here to apply for DSI’s postdoctoral fellowship program.
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.
- 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.
Items of Note
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Appointments for the third cohort of DSI Fellows start on/around July 1, 2021, with guaranteed funding for two years. In addition to a competitive salary package, DSI Fellows will also be provided with a research expenditure budget of that can be used for conference travel, computing equipment, and/or other research-related costs.
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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.
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The candidate should identify between two and five names of potential mentors who are current Columbia faculty members. A good place to look for mentors would be the list of DSI faculty affiliates but candidates are welcome to look beyond this list. If you are interested in applying data science to a specific domain, we encourage you 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.
Candidates are encouraged to apply as early as possible and preferably before December 1, 2020. For the 2021 cohort, the online application is available here. Candidates should begin to prepare the following materials:
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- A cover letter that explains your motivation for applying to this program and indicates your choice of mentors (and special interest area if applicable).
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- A curriculum vitae (including a list of publications);
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- 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;
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- Up to three representative research papers;
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- Three letters of reference submitted directly by the recommenders
- [Optional] A letter from a potential mentor indicating an interest in your candidacy and your proposed research activities, submitted directly to dsi-fellows@columbia.edu
APPLICATION SUBMISSION LINK IS NOW LIVE CLICK HERE
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Information Request Form
Postdoctoral Researchers
Arpit Agarwal
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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
Caitlin N. Dreisbach
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Data Science Institute
Postdoctoral Research Scientist
Christian Dye
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Mailman School of Public Health
Postdoctoral Research Fellow in the Department of Environmental Health Sciences
Yinqiu He
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Data Science Institute
Postdoctoral Research Scientist
Aviv Landau
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Data Science Institute
Postdoctoral Research Scientist
Abraham Liddell
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Data Science Institute
Postdoctoral Research Scholar
Meisam Mohammady
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Data Science Institute
Postdoctoral Research Scientist
Gemma Moran
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Data Science Institute
Postdoctoral Research Scientist
Annie Nigra
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Mailman School of Public Health
Postdoctoral Research Scientist in the Department of Environmental Health Sciences
Yevgeny Rakita Shlafstein
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Data Science Institute
Postdoctoral Research Scientist
Adele H. Ribeiro
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Data Science Institute
Postdoctoral Research Scientist
Alexander R. Root
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Data Science Institute
Postdoctoral Research Scientist
Aaron Schein
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Data Science Institute
Postdoctoral Research Scientist
Miranda Spratlen
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Mailman School of Public Health
Postdoctoral Research Fellow in the Department of Environmental Health Sciences
Mingzhang Yin
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Data Science Institute
Postdoctoral Research Scientist -
Irving Institute for Cancer Dynamics
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