LEAP's 2025 Summer Momentum Fellowship
- Employer
- LEAP-STC (Learning the Earth with Artificial Intelligence + Physics) at Columbia University
- Location
- New York, NY and/or Boulder, CO
- Salary
- $8,000/summer stipend
- Closing date
- Mar 19, 2025
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- Discipline
- Atmospheric Sciences, Cryosphere Sciences, Geochemistry, Global Environmental Change, Hydrology, Interdisciplinary/Other, Natural Hazards, Near Surface Geophysics, Ocean Science, Paleoceanography and Paleoclimatology, Social Sciences , Solid Earth Geophysics, Volcanology, Geochemistry, and Petrology, GeoHealth
- Career Level
- Student / Graduate
- Education Level
- PhD
- Job Type
- Internship
- Relocation Cost
- No Relocation
- Sector Type
- Academia
Job Details
LEAP’s Summer Momentum Fellowship welcomes doctoral students in data science interested in having a summer research immersion in climate data science, with the opportunity to apply their data science/machine learning skills in climate modeling and develop research interests in climate data science. Each fellow receives a summer stipend, travel support, and access to LEAP resources, such as LEAP Pangeo and workspace at the Columbia Engineering Innovation Hub.
Momentum Fellowship project leads work closely with Fellows throughout the summer on a well-defined, yet open-ended, machine learning research problem in climate data science. Project leads will also guide their Fellows to present their summer research at future LEAP events and other workshops/conferences. Read more about Summer 2025 Projects at https://leap.columbia.edu/wp-content/uploads/2025/02/2025-REU-Project-Descriptions-1.pdf.
Momentum Fellows are responsible for mentoring up to two (2) undergraduate students in the LEAP Research Experience for Undergraduates (REU) Program. Each project will host up to two (2) undergraduate students paired with one (1) Fellow.
LEAP invites applications from doctoral students in data science* (Computer Science, Statistics, Operations Research, and related fields) interested in a summer research immersion in climate data science, applying their data science/machine learning skills in climate modeling, and developing research interests in climate data science. We invite all candidates to apply.
Fellows are expected to work for 10 weeks (May 27 – August 1, 2025) and 25 hours on average each week. Each Fellow will be awarded an $8,000 summer stipend to support their research and professional growth, receive travel support, and have access to LEAP resources. Additional housing support may be available.
How do I apply? And what are the deadlines?
- The Application is available online.
- Applications are due by March 15, 2025, 11:59 p.m. (EDT).
- Letters of recommendation should be submitted via email to LEAP@columbia.edu by March 15, 2025, 11:59 p.m. (EDT).
Where will the 2025 Momentum Fellowship be located?
The 2025 Momentum Fellowship will be held at Columbia University’s Morningside Heights campus and Manhattanville Campus; some projects may be based in Boulder, CO. LEAP’s office is located in the Columbia Engineering Innovation Hub.
How much is the stipend?
The program offers a competitive stipend of $8,000 for the duration of the program. Those enrolled in a Columbia University program will receive a salary equivalent to $8,000.
Will travel be provided?
A travel stipend will be available for travel related to the project.
Will housing be provided? Are housing costs covered?
Housing support may be available.
Company
Learning the Earth with Artificial Intelligence and Physics (LEAP) is an NSF Science and Technology Center (STC) launched in 2021 with the mission to increase the reliability, utility, and reach of climate projections through the integration of climate and data science. LEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.
- Website
- http://leap.columbia.edu/
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