Remote Scientist-Research: Atmospheric Modeling & HPC

Computational Physics Inc; Glen Ellyn (IL); Remoto; USD 90.000 - 130.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive salary

Employee-owned company

Professional development support

Collaborative, science-driven culture

Descrizione del lavoro

Computational Physics, Inc. (CPI) is seeking a Full-Time Scientist-Research to support Navy weather prediction model development remotely at NRL. The role involves running models on HPCs, analyzing outputs, and driving code improvements through GitHub to meet quarterly and annual deliverables.

The candidate will work with a dispersed team, write/debug code (Fortran 2003+), and handle large atmospheric datasets (HDF5) while communicating progress in weekly meetings. U.S.

Competenze

• Bachelor's degree or higher in atmospheric science or related discipline.
• Experience with MPI Fortran, C++, or Java; strong Python/IDL/MATLAB for analysis.
• Familiarity with physics-based atmospheric models and HPC debugging.
• Knowledge of upper-atmosphere dynamics (>80 km) and data formats like HDF5.
• Ability to work in distributed, interdisciplinary teams and communicate progress on GitHub.

Mansioni

• Develop and run whole-atmosphere Navy weather models on HPCs.
• Analyze model outputs and propose code enhancements.
• Identify bugs, test fixes, and contribute new features via GitHub.
• Collaborate across a dispersed team to meet quarterly/annual deliverables.

Conoscenze

MPI Fortran

C++

Python

IDL

MATLAB

Formazione

Bachelor's degree or higher in atmospheric science or related

Strumenti

GitHub

HDF5

Descrizione del lavoro

Computational Physics, Inc. (CPI) is seeking a Full-Time Scientist-Research to support Navy weather prediction model development remotely at NRL. The role involves running models on HPCs, analyzing outputs, and driving code improvements through GitHub to meet quarterly and annual deliverables.

The candidate will work with a dispersed team, write/debug code (Fortran 2003+), and handle large atmospheric datasets (HDF5) while communicating progress in weekly meetings. U.S.