[ES_JOBS_NET] Postdoc opportunity in modeling for agroecosystem sustainability at University of Minnesota

[Zhenong Jin]

The Digital Agricultural Group at University of Minnesota is recruiting 1-2
postdocs with backgrounds in process-based modeling, geospatial data
analytics, and/or deep learning! With funding support through NSF, NASA,
DOE, and the industry, our work spread across the spectrum of process-based
modeling, data-model fusion, and algorithm development for remote sensing.
This post is looking for candidates to work on the following two topics:
1) *Modeling the agroecosystem sustainability*, with a focus on managing
greenhouse gases (GHGs) emissions and reactive nitrogen (N) and phosphorus
(P) losses from field to global scales; we’re particularly interested in
Physics Guided Machine Learning (PGML), a novel framework combines
process-based models with start-of-the-art machine learning algorithms to
leverage their complementary strengths.
2) *Crop mapping and yield prediction*, with a focus on exploring novel
satellite data and new algorithms to push the frontier of this discipline;
we’re particularly interested in high-value tree crops, and the integration
of remote sensing and process-based models.
The successful candidate will be supervised by Dr. Zhenong Jin (
http://jinlab.bbe.umn.edu/) through the College of Food, Agricultural, and
Natural Resource Sciences and work closely with diverse academic and
industrial collaborators. The position is expected to start on or after Sep
1st, 2021. The position is open till filled.

Essential Qualifications:
All applicants are expected to have a strong quantitative background and
graduate from quantitative majors such as earth and atmospheric science,
hydrology, remote sensing, environmental science, math, and statistics, or
any other closely related fields. In addition,
● Strong programming experience (e.g., Python, Fortran, or C++ in the Linux
environment) and be familiar with supercomputing and/or cloud platforms;
● Rich experience and deep understanding of process-based models;
applicants whose past research only involves running models at site-level
will not be considered;
● Rich experience in geospatial big-data analytics;
● Knowledge of machine learning beyond simple tools such as Random Forest
and ANN.

Additional Qualifications:
● Within three years of receiving the Ph.D. degree.
● Strong skill in data visualization.
● Experience with microwave satellite remote sensing.

Application Process:
Qualified candidates must send a short introduction email, including a CV
and a sample publication to Dr. Zhenong Jin (jinzn at umn.edu). Qualified
applicants will be immediately reviewed upon receiving the application
while the search may continue until the position is filled. Due to time
constraints, we only give feedback to those candidates who we plan to
interview. For further questions, please feel free to reach out to us.

-- 
Zhenong Jin
Assistant Professor
University of Minnesota - Twin Cities
Department of Bioproducts and Biosystems Engineering
Institute on the Environment Associates
http://jinlab.bbe.umn.edu/
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