Postdoctoral Researcher

Research Foundation of The City University of New York • New York, NY

1d • $75,000 - $84,000

About The Position

Thank you for considering a career with the Research Foundation of The City University of New York (RFCUNY). The team at RFCUNY is made up of dedicated, talented professionals committed to providing the services that allow CUNY researchers, faculty, and staff to focus on their intellectual curiosity and scientific discoveries. We are pleased that you are interested in exploring opportunities to join RFCUNY. 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

Requirements

• 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.
• 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 geospatial-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.

Nice To Haves

• While we consider all qualified candidates, preference will be given to those with a recent Ph.D. (2023 or later).

Responsibilities

• 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

Benefits

• RFCUNY Employee Benefits and Accruals (link to https://www.rfcuny.org/RFWebsite)

What This Job Offers

• Job Type: Full-time
• Education Level: Ph.D. or professional degree
• Number of Employees: 501-1,000 employees