Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
al. (2013). https://infoterre.brgm.fr/rapports/RP-62383-FR.pdf [7] Didier Dubois and Henri Prade. Possibility theory: an approach to computerized processing of uncertainty. Plenum Press, New York, 1988
-
George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 5 days ago
, or chronic disease management Explainable, robust, and ethical AI in medicine Explainable Artificial Intelligence (XAI) techniques, causal models, and uncertainty quantification Human-in-the-loop decision
-
multimodal omics data. The project will focus on multimodal representation learning, uncertainty quantification, and interpretable and biologically plausible modeling, with the goal of building foundational AI
-
learning for strategic reasoning and planning; uncertainty quantification and propagation, and decision under uncertainty; formal methods techniques for reactive synthesis in presence of unreliable input
-
candidate will take responsibility for supporting research activities related with the scope of the Project, such as aeroelastic design, structural analysis, uncertainty quantification, or optimum design
-
develops and applies methods for uncertainty quantification, engineering reliability, and risk & decision analysis to support optimal and sustainable decision-making in engineering and environmental systems
-
, United States of America [map ] Subject Area: Uncertainty Quantification for Life Sciences Appl Deadline: (posted 2025/10/01, listed until 2026/04/01) Position Description: Apply Position Description North Carolina State
-
potential (PPST) and therefore, in accordance with regulations, requires your arrival to be authorized by the competent authority of the MESR. This PhD project will investigate modeling uncertainties in flame
-
Associate in a university to work on a specific project. Working alongside the Digital Team at MIRICO, the Algorithm developer will develop a new algorithm to enhance the localisation and quantification
-
as possible and can handle the constraints. • Integrate them into Bayesian models to sample the posterior distribution and provide uncertainty quantification on the estimated parameters. • Generalize