Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
/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
-
Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
-
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
-
on frontier models like Large Language Models (LLMs) and multimodal foundation models. This includes topics such as safety alignment, adversarial training, jailbreaking, uncertainty quantification, and AI
-
, formal verification, proof assistants, automated proof search, and AI-guided mathematical discovery; neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous
-
-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant
-
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
-
) Familiarity with uncertainty quantification, railway or infrastructure asset management, or geographic information systems is considered an advantage Proficiency in written and spoken English is required, and
-
states are unobserved. Purely data-driven models offer flexibility, but often ignore known biology and provide limited insight into uncertainty and mechanisms. These challenges motivate a broader Biology