Postdoctoral Researcher

RFCUNY Research Foundation of the City University of New York • Brooklyn, NY

1d • Onsite

About The Position

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 pCO2 is 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

• Preference will be given to those with a recent Ph.D. (2023 or later).

Responsibilities

• Work on efficient representation, including identifying informative features and generating new ones.
• Improve machine learning modeling for better generalization and resilience, exploring new algorithms and domain adaptation strategies.
• Utilize Earth System Models (ESMs) and Global Ocean Biogeochemistry Models (GOBMs) as testbeds to understand the reconstruction process and build resilience.
• Develop custom metrics to assess the relationship between feature variables and pCO2 in models compared to observations.
• Perform other related duties as assigned.

Benefits

• RFCUNY Employee Benefits and Accruals

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree