Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark datasets and an
-
opportunities. The chance to help shape a newly funded research line with real clinical impact, within an international network of academic and industry partners. Where to apply Website https
-
learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
-
representations, and flow matching) for uncertainty-aware 3D reconstruction of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast
-
, Create insightful graphs and other graphical tools to present outcomes back to farmers, Develop reporting for specific groups of farmers that are for instance in a specific supply chain or a learning
-
refinement (e.g., correcting mislabelled records), construction and use of knowledge graphs, and transforming data between different representations. In addition, you will contribute to enriching (meta)data
-
to provide reliable predictions and support effective decision-making. The aim of the PhD project is to address these gaps by developing theoretical and computational tools that combine game theory, graph
-
generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able to incorporate non-standard extreme directions, which permits the modeling
-
, museum professionals, and other partners in the Hybrid Intelligence network. Where to apply Website https://www.academictransfer.com/en/jobs/362707/postdoc-position-on-multimodal-… Requirements Specific
-
flows, flow matching, neural ODEs as well as graph neural networks. Looking forward, the aim is to develop new mathematical and computational paradigms that can deepen our scientific understanding of deep