Computing and Mathematical Sciences Division - Postdoctoral Researcher: AI for Weather Forecasting

MBZUAI (Mohamed bin Zayed University of Artificial Intelligence); Abu Dhabi; Sur place; AED 335 000 - 469 000; Plein temps

Avantages offerts par ce poste

Comprehensive health & life insurance

Relocation support for international H

Live in Abu Dhabi

Industry & Government Collaborations

Résumé du poste

MBZUAI invites applications for a fully funded two-year Postdoctoral Researcher position in AI for Weather Forecasting under the supervision of Prof. Souhaib Ben Taieb.

The role is embedded within a collaborative research program between MBZUAI and the AI for Climate initiative at the University of Chicago, supported by funding from the International Affairs Office at the Presidential Court of the United Arab Emirates and the Gates Foundation.

Qualifications

• Experience applying machine-learning methods to weather forecasting, climate analysis, or spatial data.
• Strong background in spatiotemporal modeling, probabilistic prediction, or time-series analysis.
• Proficiency in Python; hands-on experience with PyTorch or JAX.
• Experience working with weather or climate datasets (reanalysis, satellite, observational, or forecast).
• Strong publication record in AI or domain-relevant venues and excellent English.
• PhD in relevant field (CS, Atmospheric/Climate Science, Applied Math, Statistics).

Responsabilités

• Conduct original research in probabilistic and ensemble weather forecasting, deep learning for spatiotemporal data, uncertainty quantification, data assimilation, or related areas.
• Develop and evaluate AI-based weather forecasting methods using reanalysis, satellite, observational, and forecast datasets.
• Assess forecast accuracy, calibration, extremes, and regional performance using appropriate probabilistic and deterministic metrics and reproducible benchmarks.
• Develop forecasting models suitable for operational settings, with particular emphasis on agriculturally relevant products and regional and sector-specific needs.
• Publish research in top-tier AI or domain-relevant venues and communicate findings to technical, interdisciplinary, and operational audiences.
• Collaborate with research institutions, government agencies, national meteorological and hydrological services, agricultural institutions, and practitioners; contribute to training and knowledge transfer.

Connaissances

Machine learning for weather/climate/‑

Spatiotemporal modeling

Probabilistic prediction

Time-series analysis

Python

English proficiency

Formation

PhD in Computer Science or Atmospheric/Climate Science or Applied Mathematics/Statistics

Outils

PyTorch

JAX

xarray

Dask

NetCDF

Zarr

Description du poste

Description

MBZUAI invites applications for a fully funded two-year Postdoctoral Researcher position in AI for Weather Forecasting under the supervision of Prof. Souhaib Ben Taieb. The role is embedded within a collaborative research program between MBZUAI and the AI for Climate initiative at the University of Chicago, supported by funding from the International Affairs Office at the Presidential Court of the United Arab Emirates and the Gates Foundation. The program advances AI-based weather forecasting methods and supports their operational use in low- and middle-income countries. The position includes a competitive tax-free monthly salary, housing allowance, comprehensive medical insurance, an annual return flight, and full visa application support.

Key Responsibilities

• Conduct original research in probabilistic and ensemble weather forecasting, deep learning for spatiotemporal data, uncertainty quantification, data assimilation, or related areas.
• Develop and rigorously evaluate AI-based weather forecasting methods using reanalysis, satellite, observational, and forecast datasets.
• Assess forecast accuracy, calibration, extremes, and regional performance using appropriate probabilistic and deterministic metrics and reproducible benchmarks.
• Develop forecasting models suitable for operational settings, with particular emphasis on agriculturally relevant products and regional and sector-specific needs.
• Publish research in top-tier AI or domain-relevant venues and communicate findings to technical, interdisciplinary, and operational audiences.
• Collaborate with research institutions, government agencies, national meteorological and hydrological services, agricultural institutions, and practitioners; contribute to training and knowledge transfer.

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is a world-leading university dedicated to advancing AI as a global force for good. The UAE’s visionary leaders established MBZUAI to educate and develop top talent, conduct advanced research, foster an innovation ecosystem, and act as a strategic think tank for the public and private sectors.

The University attracts world-class thinkers and doers in computer science, AI, bioscience, humanities and beyond. Our groundbreaking academic research initiatives are working on developing future solutions that will impact the world and change the way we imagine things.

Why Join MBZUAI?

• Top-Tier Compensation: Competitive salaries aligned with leading global academic institutions
• Exceptional Benefits:
  • Comprehensivehealth & life insurance
  • Relocation supportfor international hires
• Live in Abu Dhabi:World’s safest city, year-round sunshine, rich culture, and modern amenities
• Industry & Government Collaborations: Work with top AI researchers, leading tech firms, and policymakers

Qualifications

• Experience applying machine-learning methods to weather forecasting, climate analysis, or spatial data.
• Strong background in spatiotemporal modeling, probabilistic prediction, or time-series analysis.
• Proficiency in Python and hands-on experience with PyTorch or JAX.
• Experience working with weather or climate datasets, such as reanalysis, satellite, observational, or forecast data.
• Strong publication record in top-tier AI or domain-relevant venues and excellent written and spoken English.
• PhD in Computer Science, Atmospheric or Climate Science, Applied Mathematics, Statistics, or a closely related field.

Preferred Qualifications

• Experience with data assimilation, uncertainty quantification, ensemble methods, or evaluation frameworks such as WeatherBench 2.
• Experience with xarray and scalable scientific-data workflows using Dask and NetCDF or Zarr datasets.
• Experience with GPU or distributed training and familiarity with agricultural or environmental applications of weather forecasts.
• Experience working in interdisciplinary or international research collaborations.
• Advanced training in spatiotemporal modeling, probabilistic prediction, numerical weather prediction, data assimilation, or uncertainty quantification.