Academic Job Description

Soil and Crop Sciences Section

Postdoctoral Associate

 The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest college. We work across disciplines to tackle the challenges of our time through world-renowned research, education, and outreach. The questions we probe and the answers we seek focus on three overlapping concerns: We believe that achieving next-generation scientific breakthroughs requires an understanding of the world’s complex, interlocking systems. We believe that access to nutritious food and a healthy environment is a fundamental human right. We believe that ensuring a prosperous global future depends on the ability to support local people and communities everywhere. By working in and across multiple scientific areas, CALS can address challenges and opportunities of the greatest relevance, here in New York, across the nation, and around the world.

Position Function

This position will work on a DOE-funded project entitled "Amazon vs Congo: Understanding the Intercontinental Differences of Tropical Rainforests’ Responses to Climate Variability" under the supervision of Professor Ying Sun. The responsibilities of this position include synthesizing in-situ field measurements from collaborators (site PIs) in the Amazon and Congo basins, conducting machine learning-based causal inference analysis on the response of climate variability of carbon fluxes for both site and regional scales, and incorporating a novel model of solar-induced chlorophyll fluorescence (SIF) and photosynthesis into the ELM-FATES model. This Postdoctoral Associate will collaborate closely with scientists from Harvard University and Lawrence Berkeley National Lab, along with field scientists in Amazon and Congo basins.

Anticipated Division of Time

Lab Research: 60% Field Research: 10% Writing: 30%

Requirements

A PhD degree in Remote Sensing, Climate Science, Ecology, or Geosciences or related discipline is required. Preferences will be given to candidates with expertise in developing and performing climate model simulations and programing skills in Fortran, C, Matlab, NCL, R or Python. Candidates with experience in deep learning will be given special consideration.

Supervision Exercised

This position may supervise students.

To apply:

Please apply via Academic Jobs Online (https://academicjobsonline.org/ajo/jobs/28251)

Qualified candidates should submit a short cover letter, curriculum vitae, contact information for three references, and a statement supporting diverse communities (this can be a stand-alone document (preferred) or the information can be embedded in other parts of the application materials) outlining how, through research, teaching, service, mentoring, extension, and/or outreach, the candidate has and will contribute to support Cornell’s historical mission of “any person … any study,” via the website. 

Applications will be reviewed as received, continuing until a suitable applicant is identified.

CALS hiring range for this title $61,008 - $62,000.

College of Agriculture and Life Sciences

Life. Changing.

Cornell University is an innovative Ivy League and Land-grant university and a great place to work. Our inclusive community of scholars, students, and staff impart an uncommon sense of larger purpose and contribute creative ideas to further the university's mission of teaching, discovery, and engagement.

Cornell’s regional and global presence includes state-wide Cornell Cooperative Extension programs and offices in all counties and boroughs, global partnerships with institutions and communities engaged in life-changing research and education, the medical college’s campuses on the Upper East Side of Manhattan and Doha, Qatar, and the Cornell Tech campus on Roosevelt Island in the heart of New York City.

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Cornell University embraces diversity and seeks candidates who will contribute to a climate that supports students, faculty, and staff to all identities and backgrounds. We strongly encourage individuals from underrepresented and/or marginalized identities to apply.