Postdoctoral Research Scientist

Postdoc position in the NSF-funded LEAP (Learning the Earth through Artificial Intelligence and Physics) STC (Science and Technology Center)

The National Science Foundation-funded Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC), https://leap.columbia.edu/ , a large multi-institutional center effort meant to improve climate projections using novel artificial intelligence for better climate adaptation, invites applications for Postdoctoral Research Scientist positions in the fields of climate science and data science. 

In your application materials, please indicate to which project you are applying, or rank your preference if you are interested in applying for more than one. Applications will be reviewed on a rolling basis, but applications received by April 15th are guaranteed to receive consideration.

• Project 1: Attributing Climate Model Divergence with Hierarchical Representation Transformers 

PI: David John Gagne; co-Is: Michael Pritchard, Carl Vondrick

Location: NSF NCAR, Boulder, Colorado

The goal of this project is to develop an automated approach to identify systemic differences between climate simulations across different spatial scales and attribute those differences to particular model components.  One of the most challenging and time-consuming aspects of climate model development is determining how adding new components to the modeling system changed the model climate. While some changes can be quite obvious and significant, others are much more subtle and are harder to attribute to a particular interaction within the model. In some instances, climate scientists have spent years tracking down the source of these changes, delaying the deployment of new models or interfering with the results from expensive runs. For this project, we intend to utilize recent advances in encoding multiscale data and learning relationships with newer transformer models and machine learning tasks that compare two items to accomplish this task. The transformer models will be trained to distinguish different forcing scenarios, and then we will use explainable AI (XAI) attribution methods to identify the model inputs that most affected the predictions. We will examine how the choice of encoding and the scale of the XAI perturbation affect the resulting attribution and how closely the attribution is associated with changes in forcing mechanisms.  If successful, this approach could speed up the debugging and iteration process of incorporating new model components (ML or physics-based) into CESM and other Earth System Models, enabling a leap ahead in model development.

The postdoc will be supervised by PI Gagne (primary supervisor; NSF NCAR/LEAP) and will benefit from mentoring by Profs. Pritchard and Vondrick as well as the NSF NCAR Machine Integration and Learning for Earth Systems (MILES) group. It is expected that the postdoc has a prior background in machine learning with significant experience with a major deep learning framework along with the ability to set independent goals, work with interdisciplinary teams, and communicate clearly. Prior climate science experience and/or a willingness to learn about climate science is strongly encouraged.

• Project 2: The Metrics Reloaded: Improved similarity assessment for climate maps

PI: Viviana Acquaviva; co-Is: Sara Shamekh, Duncan Watson-Parris

Location: Columbia University, New York, NY

Recent improvements in climate modeling and machine learning methods give us more opportunities to reduce uncertainties in future climate projections, which is crucial to planning for adaptation and mitigation measures. One important consideration is assessment of output similarity between models and data, or among different models. Various classic measure of error, such as the mean square/absolute error (MSE/MAE) of differences between cell values in a gridded map, have been used as a summary statistic of relevance, but they might not be suitable to capture the complexities of maps, where different spatial and temporal scales may be at play. We propose to develop, test, and validate improved metrics for climate models, beginning from assessing differences in static maps. We formulate a plan to use improved metrics beyond model skill assessment, as a foundation for dimensionality reduction, model comparison, equation discovery, and visualization purposes.

The postdoc will be supervised by PI Acquaviva (primary supervisor; CUNY/LEAP) and also benefit from mentoring from Profs. Shamekh (NYU) and Watson-Parris (UCSD). It is expected that the postdoc will have a genuine curiosity and interest for data exploration and analysis, the ability to set independent goals and communicate clearly, and a background in either climate science or data science. 

• Project 3: Deep convection emulation and role of cloud organization

PI: Pierre Gentine; co-Is: Stephan M Mandt, Mike Pritchard

Location: Columbia University, New York, NY

Deep convection is one of the major sources of uncertainties in climate models and especially to constrain the hydrologic cycle. The postdoc will work on the development of a new deep convective algorithm based on deep learning, in the Community Earth System Model (CESM). The algorithm will include the role of convective aggregation and evaluate its impact on regional to local scale. The postdoc will also develop new theoretical developments to understand the stability of hybrid (physics and machine learning) models. The work will use high-resolution storm resolving simulations as well as observational products to define a state-of-the-art convective parameterization. Some previous experience with either convective parameterizations or analysis of deep convection would be ideal, yet not required. 

The postdoc will be supervised by PI Pierre Gentine and collaborate with Profs. Mike Protichard and Stephan M Mandt at UC Irvine.

Minimum Qualifications for Projects 1-3

• A PhD. in Data Science, Computer Science, Physics, Earth System Science or a directly related discipline is required by the start of the appointment.
• Strong programming skills.
• Excellent command of the English language (oral and written).

Preferred Qualifications for Projects 1-3

• Fluency in Python.
• Advanced experience with machine learning/deep learning algorithms and libraries.
• Experience in statistical/mathematical analyses of model output and/or observational datasets.   
• Strong communication skills.
• Experience collaborating on interdisciplinary teams.

Application Instructions for Projects 1-3

Applications to any of those positions must state to which postdoc project they are applying, and include: (a) curriculum vitae; (b) A 2-page statement of research interests and how they connect to the chosen project(s); (c) names of at least three references who may be asked to provide letters. 

Applicants should apply via Columbia University’s website (job requisition # 135476), at this link: https://academic.careers.columbia.edu/#!/135476?keywords=leap&sortKey=keywordScore  

• Project 4: Improving the parameterization of atmospheric boundary layer using physics-informed machine learning

PI: Sara Shamekh

Location: New York University, New York, NY

The Courant Institute at New York University (NYU) is seeking expression of interest from a highly motivated Postdoctoral Associate to join our team working on improving the representation of sub-grid scale physics for climate models. This effort is part of the Learning Earth with Artificial Intelligence and Physics (LEAP), an NSF-funded Science and Technology Center. The scientific goal of this project is to enhance climate projections by reducing errors in climate models, specifically those rooted in the representation of the atmospheric boundary layer, using machine learning (ML).

The successful candidate will collaborate with Profs. Sara Shamekh (NYU) and Pierre Gentine (Columbia University), and with a team of interdisciplinary researchers, to develop and implement ML techniques for climate phenomena that span multiple scales and aspects of physics. This includes a focus on non-local boundary layer turbulence and shallow clouds.

This full-time appointment is available immediately. It is initially for one year, with the possibility of renewal for up to three years, subject to satisfactory performance and available funding.

In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $62,500 - $70,000. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer. 

Qualifications for Project 4:

• Completion of a PhD in physics, mathematics, computer science, engineering, statistics, or a related field at the time of the appointment;
• Strong programming experience;
• Strong interest in the application of machine learning to science and engineering problems;
• A record of relevant publications in the peer-reviewed scientific literature appropriate to their career stage;
• Ability to work independently and as part of an interdisciplinary team.
• Ability to work in a fast-paced environment

Application Instructions for Project 4: For full consideration, applicants should submit via NYU’s Interfolio website, the following by June 30th, 2024:

• a Curriculum Vitae with a list of publications,
• a cover letter (no more than 2 pages) detailing their research experience, how their interests would fit the project, career plans, and available start date,
• 3 letters of recommendation.

  For additional information, please contact Sara Shamekh ([email protected] )

Please visit this link to apply: https://apply.interfolio.com/142981  

Commitment to Diversity:

One of LEAP’s goals is to increase the diversity in climate science and data science. We welcome and encourage applications from individuals of all backgrounds and identities. We are committed to building a diverse and inclusive community and believe that a variety of perspectives and experiences is essential to advancing our research and mission.