Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NTNU - Norwegian University of Science and Technology
- Delft University of Technology (TU Delft)
- NTNU Norwegian University of Science and Technology
- Amsterdam UMC
- CNRS
- Centrum Wiskunde en Informatica (CWI)
- Eindhoven University of Technology (TU/e)
- SciLifeLab
- University of Oslo
- Uppsala universitet
- Wageningen University & Research
- BRGM
- Chalmers University of Technology
- Constructor Knowledge Labs gGmbH
- Fondazione Bruno Kessler
- Forschungszentrum Jülich
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- Helmholtz-Zentrum Dresden-Rossendorf •
- Imperial College London
- Inria, the French national research institute for the digital sciences
- Monash University
- Norwegian University of Life Sciences (NMBU)
- REQUIMTE - Rede de Quimica e Tecnologia
- Technical University of Munich
- The University of Manchester
- UNIVERSITAT POMPEU FABRA
- University of Birmingham
- University of Exeter
- University of Warwick;
- 19 more »
- « less
-
Field
-
imaging methods that are more robust and computationally efficient, while also providing a natural framework for uncertainty quantification and experimental design. A central question is whether suitable
-
mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
-
for environmental pollution sciences. It focuses on robustness to distribution shifts, degraded inputs, and out-of-distribution conditions, while also addressing uncertainty quantification, explainability, fairness
-
/research/air-water-and-landscape-science ). Duties The PhD project will focus on uncertainty quantification in modelling flow and solute transport in fractured rocks. The work will include numerical
-
is to obtain imaging methods that are more robust and computationally efficient, while also providing a natural framework for uncertainty quantification and experimental design. A central question is
-
inference, counterfactual explanations, or uncertainty quantification in deep learning Evidence of high quality scientific writing, publications, a strong master's thesis, research software, or relevant open
-
estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear stress fields); design computational pipelines that integrate image-based anatomy, blood flow physics, and uncertainty quantification
-
reliable and accountable, such as uncertainty quantification, interpretability, alignment, monitoring and human oversight, taking into account relevant standards and regulation, including the EU AI Act
-
simplification. Particular attention will be paid to uncertainty quantification and to the definition of criteria that assess the contribution of each simulation to the improvement of the prediction
-
Image processing and computer vision Experimental data analysis and uncertainty quantification Piezoelectric actuation, acoustic systems or electronic driver development Eligibility and Project