European Geosciences Union (EGU)
Overview
PhD student in Soil Modeling at Columbia University. This position is part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) coordination unit and the Soil System Research Group, focusing on environmental data science with AI-driven model ensemble methods that assimilate observation data to generate improved model outcomes.
Responsibilities
Develop novel artificial intelligence–driven model ensemble methods that assimilate observation data to generate improved model outcomes.
Engage in national model intercomparison and ensemble efforts to improve estimates of soil carbon and nitrogen dynamics.
Harmonize datasets across different scales, conduct uncertainty analysis, and design platforms to visualize model outcomes to inform agricultural management decisions and policymaking.
Collaborate with a large network of soil modeling groups.
Qualifications
Recently completed or nearing completion of a B.S./B.A. in agronomy, soil science, environmental data science, earth system science, ecology, remote sensing, computer science, or a closely related field.
Demonstrated knowledge of environmental or ecosystem processes.
Strong data science skills including geospatial analysis and cloud computing.
Proficiency in coding languages such as R, Python, and Google Earth Engine.
Excellent oral and written communication skills with the ability to network and collaborate effectively.
For international applicants, follow the instructions of Columbia’s information for international applicants; minimum English requirement is TOEFL 100 or IELTS 7.5.
About DEES The Department of Earth and Environmental Sciences (DEES) at Columbia University hosts one of the top‑rated earth and environmental science programs internationally. The U.S. News rankings highlight Columbia’s Environmental Sciences and Earth Sciences programs.
How to apply Prospective students are encouraged to contact Dr. Yushu Xia with the subject line “Prospective PhD student” before applying. In your email, please state your research interest and include a curriculum vitae. If you are an international student, please also confirm that you meet the minimum English requirements.
#J-18808-Ljbffr
Responsibilities
Develop novel artificial intelligence–driven model ensemble methods that assimilate observation data to generate improved model outcomes.
Engage in national model intercomparison and ensemble efforts to improve estimates of soil carbon and nitrogen dynamics.
Harmonize datasets across different scales, conduct uncertainty analysis, and design platforms to visualize model outcomes to inform agricultural management decisions and policymaking.
Collaborate with a large network of soil modeling groups.
Qualifications
Recently completed or nearing completion of a B.S./B.A. in agronomy, soil science, environmental data science, earth system science, ecology, remote sensing, computer science, or a closely related field.
Demonstrated knowledge of environmental or ecosystem processes.
Strong data science skills including geospatial analysis and cloud computing.
Proficiency in coding languages such as R, Python, and Google Earth Engine.
Excellent oral and written communication skills with the ability to network and collaborate effectively.
For international applicants, follow the instructions of Columbia’s information for international applicants; minimum English requirement is TOEFL 100 or IELTS 7.5.
About DEES The Department of Earth and Environmental Sciences (DEES) at Columbia University hosts one of the top‑rated earth and environmental science programs internationally. The U.S. News rankings highlight Columbia’s Environmental Sciences and Earth Sciences programs.
How to apply Prospective students are encouraged to contact Dr. Yushu Xia with the subject line “Prospective PhD student” before applying. In your email, please state your research interest and include a curriculum vitae. If you are an international student, please also confirm that you meet the minimum English requirements.
#J-18808-Ljbffr