Postdoctoral Fellow: ML Climate & Weather Emulators

1000scholars; Bergen; In loco; NOK 617.000 - 754.000; Tempo pieno

Vantaggi offerti da questo lavoro

Pension fund enrollment

Welfare benefits

Descrizione del lavoro

University of Bergen (UiB) invites applications for a Postdoctoral Research Fellow position in the physics of weather and climate and their representation through machine learning. The appointment is for 3 years as part of the RAPTURE ERC-funded project.

The role focuses on developing ML emulators and physics-based diagnostics for extreme rainfall scenarios. The successful candidate will work within RAPTURE, train ML emulators, assess emulator performance from a weather-focused perspective, and

Competenze

• PhD or equivalent in atmospheric physics, meteorology, climate science or machine learning. PhD students may apply if defence is completed by March 2027.
• Strong mathematical and analytical skills are required.
• Interest in meteorology of extreme events and ML for the earth system is required.
• Strong and demonstrated programming skills.
• Experience with geospatial data analysis in Python, scientific HPC environments, ML frameworks (e.g. PyTorch) and GitHub is advantageous.
• Ability to work independently and collaboratively with good English skills.

Conoscenze

Mathematical skills

Analytical skills

Programming skills

Python (geospatial)

PyTorch

GitHub / version control

Formazione

PhD or equivalent in atmospheric physics / meteorology / climate science or ML

Strumenti

PyTorch

Python

HPC environments

GitHub

Descrizione del lavoro

University of Bergen (UiB) invites applications for a Postdoctoral Research Fellow position in the physics of weather and climate and their representation through machine learning. The appointment is for 3 years as part of the RAPTURE ERC-funded project.

The role focuses on developing ML emulators and physics-based diagnostics for extreme rainfall scenarios. The successful candidate will work within RAPTURE, train ML emulators, assess emulator performance from a weather-focused perspective, and