Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Heidelberg University
- Basque Center for Applied Mathematics
- CNRS
- DTU Electro
- Delft University of Technology
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); today published
- Erasmus University Rotterdam
- Forschungszentrum Jülich
- GFZ Helmholtz Centre for Geosciences
- Helmholtz Zentrum Hereon
- Helmholtz-Zentrum Geesthacht
- Institut Pasteur
- Instituto Superior de Agronomia
- Ludwig-Maximilians-Universität München •
- Maastricht University (UM)
- Monash University
- Nature Careers
- New York University
- Newcastle University;
- Swansea University
- Technical University Of Denmark
- Technical University of Munich
- UNIVERSITY OF HELSINKI
- University Hospital Jena
- University of Amsterdam (UvA); Published yesterday
- University of Birmingham
- University of Cambridge
- University of East Anglia;
- University of Exeter
- University of Massachusetts Medical School
- University of Newcastle
- University of Sheffield;
- University of Tübingen
- University of Warwick
- Utrecht University
- Utrecht University; Utrecht
- 27 more »
- « less
-
Field
-
. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
-
-represented backgrounds. The objective of the research project is to perform Bayesian inversion to characterise the velocity field of 3D partial differential equations describing brain fluid and solute movement
-
learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
-
these changes affect ecosystem functions. To extend these analyses to new types of data and questions, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more
-
for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
-
Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
-
opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
-
PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of machine learning, uncertainty quantification, and Bayesian modelling. They will provide complementary expertise to bridge agentic AI with real-world impact. What We Are Looking from You Background in
-
University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 4 hours ago
modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration) Strong programming skills in R or Python; familiarity with Bayesian
-
using MRI scans. DC1 will extend this framework to regional normative models using Bayesian regression and Generalized Additive Model for Location, Scale and Shape (GAMLSS) to derive age- and region