Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Kansas Medical Center
- University of Oslo
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Zintellect
- Cambridge, University of
- George Mason University
- Imperial College London
- Lunds universitet
- NORCE Norwegian Research Centre
- Nansen Environmental and Remote Sensing Center
- UNIVERSITY OF SOUTHAMPTON
- University of Aberdeen;
- University of Birmingham
- University of Lund
- 4 more »
- « less
-
Field
-
are seeking a highly motivated candidate to strengthen our enthusiastic research group, Data Assimilation and Optimization, working at the forefront of ensemble-based data assimilation methodology, optimization
-
for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
-
principles combined with data assimilation. ML is also used in generative mode to provide several possible outcomes of the forecast. Today, operational forecasts of the Arctic Ocean are also provided by
-
development work could focus on moving towards hyper-resolution land surface representations, assimilation or observations, or downscaling and improvement of forcing data. Location: Jet Propulsion Laboratory
-
reduction, with an emphasis on maintaining physical consistency, numerical stability, and real-time data assimilation within reduced-order models. Primary application areas include computational physics and
-
14 Mar 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Environmental science Geosciences Researcher Profile Recognised Researcher (R2) Established
-
to revolutionize agriculture in Morocco by combining cutting-edge technologies, including crop growth models, remote sensing data, data assimilation, machine learning, and seasonal weather forecasts. As a
-
assimilation improve the ability of Flow MRI to be used not just to visualise flow but to infer rheological behaviour directly from experimental data. This project will develop these capabilities by combining
-
an emphasis on maintaining physical consistency, numerical stability, and real-time data assimilation within reduced-order models. Primary application areas include computational physics and climate modeling
-
notebook, prepare publication-quality manuscripts (including figures and tables) and publish in peer-reviewed literature. Read literature articles, develop new ideas, and assimilate the information into his