Postdoctoral Associate - CIMAS

University of Miami; Miami (FL); On-site; USD 50,000 - 70,000; Full time

Job summary

A leading academic institution in Miami seeks a full-time Post-Doctoral researcher focused on the impact of ocean observations on hurricane prediction models. Ideal candidates will have a Ph.D. in oceanography or related fields, proven numerical modeling experience, and strong programming skills. Responsibilities include conducting Observing System Experiments, improving ocean models, and publishing findings. This role offers the chance to collaborate closely with NOAA and other scientific partners.

Qualifications

• Demonstrated experience with running numerical ocean models.
• Capability in data assimilation and troubleshooting.
• Proven publication record and scientific writing skills.

Responsibilities

• Improve existing high-resolution Modular Ocean Model version6.
• Conduct ocean Observing System Experiments for hurricane forecasts.
• Analyze results and present findings in scientific meetings.

Skills

Running numerical ocean models

Data assimilation

Troubleshooting capabilities

Knowledge of Linux

FORTRAN

Python

C++

Machine learning

Parallel computing

GPU programming

Education

Ph.D. in oceanography, applied mathematics, statistics or related discipline

Tools

Matlab

R

IDL

Job description

Overview

The Cooperative Institute for Marine and Atmospheric Studies (CIMAS) of the University of Miami, Rosenstiel School of Marine, Atmospheric, and Earth Science has an exciting opportunity for a full‑time Post‑Doctoral researcher position based in Miami, Florida. This position is supported for a period of two years through a project funded by the NOAA Global Ocean Monitoring and Observing (GOMO) program. The primary objective of this role is to undertake a comprehensive investigation to quantify the impact of various ocean observations on the accuracy and robustness of the Marine JEDI analysis system. A main focus involves systematically evaluating how the assimilation of diverse oceanographic data affects the initial state of the ocean models. This analysis is fundamental to understanding and ultimately enhancing the skill of coupled ocean‑atmosphere models used for tropical cyclone and hurricane prediction. The incumbent is expected to: perform ocean Observing System Experiments (OSEs) to assess the impact of ocean observations, particularly in‑situ observations from various platforms, such as gliders, on ocean analysis; conduct ocean OSEs for hurricane forecasts, using weakly coupled data assimilation integrated into the coupled Hurricane Analysis and Forecast System (HAFS). The appointee will work closely with scientific advisors at the PhOD, as well as partners at the Hurricane Research Division of NOAA/AOML and at the Environmental Modeling Center (EMC) of the U.S. National Weather Service.

Department Specific Functions

• Improve existing high-resolution Modular Ocean Model version6 (MOM6) regional model to minimize model errors.
• Conduct MOM6 analysis using the Marine JEDI data assimilation (DA) system with a variational DA approach.
• Perform ocean OSEs, with various DA cycling and different data windows, and conduct hurricane predictions using the coupled Hurricane Analysis and Forecast System (HAFS).
• Analyze results and present findings in scientific meetings and publish results in peer‑reviewed journals.

Minimum Qualifications

• Applicants must have a Ph.D. in oceanography, applied mathematics, statistics or a closely related discipline.
• Required skills include demonstrated experience with running numerical ocean, atmosphere, or coupled Earth system model, and/or data assimilation, and troubleshooting capabilities.
• Desirable skills include working knowledge of Linux, FORTRAN, Python and C++; experience with machine learning, parallel computing and high‑performance computing environments, and GPU programming.
• Necessary skills include knowledge of data processing using software (e.g., Matlab, R, IDL) and/or statistical/mathematical programming languages (e.g., R, Matlab, Python).
• The incumbent must have a proven publication record, scientific writing skills, and communication skills.
• The successful candidate must be able to work independently as well as collaboratively with internal and external partners.

Job Details

Job Status: Full time

Employee Type: Staff

Equal Opportunity Employer

The University of Miami is an Equal Opportunity Employer. Applicants and employees are protected from discrimination based on certain categories protected by Federal law. Click here for additional information.