Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
-
, Phnom Penh) using collected entomological time series; - Perform global sensitivity analyses (Sobol, FAST, and equivalents) to identify dominant parameters and key interactions; - Quantify uncertainty
-
candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
-
, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
-
Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a
-
application! We invite applications for a fully funded PhD student position to join the research group of Jan Glaubitz to work on Bayesian Computational Mathematics for reliable and trustworthy uncertainty
-
to characterise the properties of radio antennas as used in modern radio interferometers (e. g., MeerKAT, LOFAR, SKA). The goal is to establish accurate models for antenna responses and describe their uncertainties
-
algorithms for Robust Relative Biological Optimization, considering uncertainties in the parameters of dose-response models. Finally, the aim is to o assess the potential of fractionation optimisation in
-
This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
-
This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification