FESOM/fesom2
Feb 2026 – Feb 2026
1 Month active
Languages Used
FortranMATLAB
Technical Skills
FortranFortran programmingfile organizationneural networksocean modelingparallel computing
Rajka Juhrbandt
PROFILE
Rajka Juhrbandt
During February 2026, contributed to the FESOM/fesom2 repository by developing a neural network module for tracer corrections within the ocean modeling workflow. This work involved integrating Fortran-based neural network operations for feature extraction and inference, enabling data-driven improvements to tracer prediction accuracy. The implementation included establishing new data processing pathways that leverage simulation environment features, supporting more robust scientific computing. Additionally, reorganized the project’s directory structure by relocating the Soufflet mesh generation script, ensuring better accessibility and maintainability. The work demonstrated proficiency in Fortran, parallel computing, and project structure management, laying groundwork for future machine learning integration.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
2,081
Activity Months1
Your Network
47 peopleSame Organization
@awi.de19
Christian StepanekMember
dsidorenMember
Patrick ScholzMember
Vera SidorenkoMember
chrisdaneMember
Jan StreffingMember
Lars AckermannMember
ackerlarMember
Laszlo H HajduMember
Shared Repositories
28
Work History
February 2026
3 Commits • 1 Features
Feb 1, 2026February 2026 performance summary for FESOM2 development. Delivered a first-in-class neural network module to enable tracer corrections within the ocean modeling workflow, integrated with the FESOM2 framework for feature-based extraction and inference. Implemented initial data processing pathways that leverage simulation environment features to drive improved predictions and data-driven corrections. Reorganized repository structure to support ML components and ensure maintainability.
3 Commits • 1 Features
Feb 1, 2026February 2026 performance summary for FESOM2 development. Delivered a first-in-class neural network module to enable tracer corrections within the ocean modeling workflow, integrated with the FESOM2 framework for feature-based extraction and inference. Implemented initial data processing pathways that leverage simulation environment features to drive improved predictions and data-driven corrections. Reorganized repository structure to support ML components and ensure maintainability.
February 2026
Activity
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Quality Metrics
Correctness86.6%
Maintainability93.4%
Architecture93.4%
Performance86.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
FortranMATLAB
Technical Skills
FortranFortran programmingfile organizationneural networksocean modelingparallel computingparallel programmingproject structure managementscientific computing
Repositories Contributed To
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