Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nanyang Technological University
- National University of Singapore
- University of Oslo
- Zintellect
- FEUP
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- University of Nottingham
- University of Texas at Austin
- Central Michigan University
- Fundació Hospital Universitari Vall d'Hebron- Institut de recerca
- Northeastern University
- UCL;
- UNIVERSITY OF NOTTINGHAM NINGBO CHINA
- University of Aveiro
- University of London
- University of the West of England
- 6 more »
- « less
-
Field
-
mechanisms, forecasting performance, return period, and multi‑sectoral impacts using high‑resolution meteorological data, satellite imagery, and climate models. It evaluates both natural drivers (El Niño
-
) (PDF) under the protocol established with +ATLANTIC - “Dynamic downscaling of European CAMS air quality forecasts to high resolution over Portugal”, CV 181/2024 of Centre for Environmental and Marine
-
practice composition and performance; and first response services in rural communities, using a range of methodologies including realist evaluations, systematic reviews, scheduling and data forecasting
-
Candidate (DC) to work on Work Package 3. As DC11, your research objectives are to: Harmonise genomic workflows and integrate AI for variant detection and outbreak forecasting Build secure, interoperable and
-
, environmental hazards, and human health using integrated observations, forecasting systems, artificial intelligence, and statistical approaches. The team works across multiple spatial and temporal scales
-
Description We are hiring 1 Research Fellow in computational science and machine learning for extreme weather forecasting applications. The ideal candidate should be skilled in coding, and software development
-
vaccination barriers and facilitators, develop forecasts of vaccine coverage for existing and novel vaccines (e.g. HPV, RSV, malaria), and support the design and implementation of small area estimation and
-
: • Develop and benchmark multimodal AI / foundation-model approaches for spatiotemporal forecasting. • Build reproducible AI training and evaluation pipelines, as well as uncertainty quantification
-
generation algorithm based on different approaches to improve understanding the behavior of forecasting algorithms in time series and tabular data. The workplan will be as follows: Literature review Design of
-
ecology, environmental forecasting and climate adaptation, fisheries and ecosystem-based management, ecological engineering and restoration, environmental sociology, and sustainability science. The Nature