Thank you for considering a career with the Research Foundation of The City University of New York (RFCUNY)! We are thrilled that you are interested in exploring opportunities to join our team.
Primary Location:
NYC COLLEGE OF TECHNOLOGY
Bargaining Unit:
Yes
The Terra D2I (Data To Insights) lab at the CUNY New York City College of Technology, led by Dr. Viviana Acquaviva, is seeking a highly motivated postdoctoral researcher to join a growing research group for the project "From sparse data to full spatio-temporal fields: surface ocean carbon and beyond", sponsored by the Simons Foundation.
Project Overview
This project aims to reconstruct the global surface ocean pCO2 field, starting from observations that are extremely sparse in space and time. Because of data sparsity, the reconstruction of the full field relies on additional information that can be measured from satellites, such as the temperature and salinity of the ocean. These become the features of a machine learning model that is trained to predict pCO2using the available observations as a learning set. The predictions for the ML model are then used for "infilling" or reconstructing the full pCO2 field, which serves to estimate the global ocean carbon sink. This is a naturally difficult problem for ML methods, because there is an unsolvable distribution shift between the training domain (where observations are available) and the application domain (all other points in space and time). The project's objective is to improve this reconstruction, making it more accurate and robust.
The tools that we use include classical statistics, Bayesian parameter inference, and machine learning. We collaborate with a broad community of researchers, from statisticians to physical oceanographers to climate modelers to cosmologists.
Key Responsibilities
The postdoctoral researcher will work on one or more of these aspects:
Efficient representation - What are the most informative features to use for this task? Can we generate new ones?
Better ML modeling - Everything about improving the machine learning modeling and making it more resilient to generalization, from new algorithms that capture relational inductive biases to domain adaptation strategies to equation discovery.
Tests of Generalization - The predicted global pCO2 field derived from the infilling is a crucial input for the Global Carbon Budget, but we can't test its accuracy directly. We use Earth System Models (ESMs) and Global Ocean Biogeochemistry Models (GOBMs) as testbeds to better understand the reconstruction process and to build resilience into our representation and ML modeling above.
Testing ESMs and GOBMs: Through our work on optimal representation, we also plan to develop custom metrics to assess how well the relationship between feature variables and pCO2is captured in the models, compared to the observations.
Other related duties as assigned.
The lab also anticipates hiring a post-baccalaureate researcher in Fall/Winter 2025 and a Ph. D. student with starting date in Spring or Fall 2026 to work on related projects. The postdoc will participate in co-mentoring at least one junior researcher and will have many opportunities for further professional development, decided together with the PI and according to their professional goals and interests.
Additional responsibilities include occasional travel (once or twice a year) to conferences and workshops to present research.
Additional information
The target start date for this position is between January and March 2026. The contract is renewable on a yearly basis for up to 3 years of total duration. The starting salary range for this position is $75,000-$82,000, commensurate with experience and skills. An annual travel budget of $8,000 and a separate budget for computer supplies and publication support are also available.
This is a full time, in-person position; candidates are expected to be in the office at least three days a week.
Application Instructions
For full consideration, applicants should submit the following materials by November 15th, 2025:
a Curriculum Vitae with a list of publications;
a cover letter (no more than 2 pages) describing their research experience, available start date, career plans, and how their interests and skills would fit the project. Please also include the names of 3 references that could be contacted to request confidential letters of recommendation.
For additional information, please contact Dr. Viviana Acquaviva at [email protected].
Qualifications:
Ph.D. in the physical or mathematical sciences, in climate science, or a closely related relevant discipline. Applicants may be ABD but must have received their degree by the appointment start date. While we consider all qualified candidates, preference will be given to those with a recent Ph.D. (2023 or later).
Strong self-motivation, curiosity, a genuine interest in the topic of Climate Data Science, a collaborative mindset, and the desire to join a truly interdisciplinary community.
Strong programming experience in Python.
Advanced mathematical modeling and statistical modeling skills.
Familiarity with Machine Learning algorithms and pipelines (building, testing, and improving models) and/or geospatio-temporal data analysis.
Excellent mastery of written and spoken English.
A record of relevant publications in the peer-reviewed scientific literature appropriate to career stage.
Pay Range:
$75,000 - $82,000
RFCUNY Benefits
(link to https://www.rfcuny.org/RFWebsite )
About the Research Foundation
The Research Foundation of The City University of New York (RFCUNY) is a nonprofit educational corporation founded in 1963 to provide post-award fiscal and administrative support for CUNY’s research and sponsored programs. RFCUNY’s services allow CUNY researchers, faculty, and staff to focus on their intellectual curiosity and scientific discoveries, on projects and programs that serve our local and global communities, proposing concrete solutions to society’s most pressing challenges.
RFCUNY serves as a fiscal agent and works closely with all the CUNY campus Grants Offices to perform the core functions of post-award financial management for CUNY research projects and sponsored programs. These functions include legal assessment and signing of agreements where RFCUNY is named as a fiscal agent; setting up award accounts; preparing sub-awards and assisting PIs in monitoring the work of the recipients of sub-awards; supporting project directors with hiring and managing research project and sponsored program staff; supporting the purchasing and paying for goods and services with grant and program funds; managing financial aspects of projects including accounts receivable, financial reporting, invoicing, budget monitoring, and cost compliance with uniform guidance; ensuring that sponsor financial requirements are met; monitoring compliance with applicable project and financial management rules and laws; supporting the management of independent and external audits and financial reviews; and providing data, information, management expertise, and other supports to CUNY’s research and sponsored programs.
Equal Employment Opportunity Statement
The Research Foundation of the City University of New York is an Equal Opportunity/Affirmative Action/Americans with Disabilities Act/E-Verify Employer. It is the policy of the Research Foundation of CUNY to provide equal employment opportunities free of discrimination based on race, color, age, religion, sex, pregnancy, childbirth, national origin, disability, marital status, veteran status, sexual orientation, gender identity, genetic information, marital status, domestic violence victim status, arrest record, criminal conviction history, or any other protected characteristic under applicable law.
