Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- CNRS
- The University of Southampton
- University of Liverpool
- University of Nottingham
- University of Oslo
- Argonne
- Center for Biologics Evaluation and Research (CBER)
- Center for Drug Evaluation and Research (CDER)
- Centro de Engenharia Biológica da Universidade do Minho
- Curtin University
- Indiana University
- Institut de Físiques d'Altes Energies (IFAE)
- King Abdullah University of Science and Technology
- Nature Careers
- Technical University of Munich
- Tilburg University
- University of Birmingham
- University of Liverpool;
- University of Oxford
- University of Oxford;
- University of South Carolina
- University of Texas at El Paso
- Zintellect
- 13 more »
- « less
-
Field
-
hundreds of hours of exposure) in order to estimate systematic errors. - Develop open-source analysis pipelines for extracting diffuse emission from objects with very low surface brightness. Take into
-
students. The post holder will develop an analytical framework to achieve the grant objectives. Specifically, the postholder will write and publish scientific papers that test whether local extirpation is
-
develop an analytical framework to achieve the grant objectives. Specifically, the postholder will write and publish scientific papers that test whether local extirpation is prompted by the same
-
energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of
-
Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference
-
entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
-
physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
-
tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
-
national scale programmatic decisions and policy. Specific activities include: Operationalize, and where needed, improve Bayesian spatio-temporal feral swine abundance model allowing predictions to be
-
. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models