Post-doctoral researcher in numerical ocean modeling – Rutgers University

Post-doctoral researcher in numerical ocean modeling

The Earth System Modeling (ESM) Lab in the Department of Environmental Sciences at Rutgers University is seeking up to two post-doctoral researchers to work in the area of regional ocean modeling of coastal areas.

The positions involve regional implementations of the NOAA-GFDL MOM6 ocean model in the Northwest Atlantic ocean. The candidates will assist in the development of the regional configurations, running the model for both hindcast and downscaled climate projections, analysis and scientific reporting of the model outputs. The positions are related to projects focusing on the dynamics of the circulation on the continental shelf, impacts of climate change on marine ecosystems and studies related to the potential impacts of offshore wind structures on fisheries in the mid-Atlantic bight.


The position has a minimum requirement of a Ph. D. in a related field (oceanography, atmospheric sciences, engineering or physical sciences). Candidates should have experience in configuring and running ocean models, working with diverse datasets and interpreting results leading to scientific publications. A working knowledge of FORTRAN, python and the Unix environment is necessary.

Desired skills:

Background in numerical methods in Earth Science and experience with climate models is strongly preferred. Experience with high-performance and cloud computing environments and knowledge of ocean and atmosphere datasets is desired. Prior work with the NOAA-GFDL MOM6 model is also desired.

Applications can be submitted online at:

Complete applications, including a CV with a list of publications, a statement of research interests (no more than 2 pages including references), and contact information of 3 references can be submitted at

Please direct inquiries by email to:

Prof. Enrique Curchitser


The Earth System Modeling lab strives to be a diverse working group and strongly

encourages applications from under-represented groups.

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