Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- Radboud University
- University of Antwerp
- Wageningen University & Research
- Amsterdam UMC
- Inria, the French national research institute for the digital sciences
- KU LEUVEN
- NTNU Norwegian University of Science and Technology
- Tilburg University
- University of Exeter
- University of Twente (UT)
- Aalborg University
- Academic Europe
- Constructor Knowledge Labs gGmbH
- Forschungszentrum Jülich
- Grenoble INP - Institute of Engineering
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.
- Leipzig University •
- Luleå tekniska universitet
- Manchester Metropolitan University
- Monash University
- NOVA.id.FCT- Associação para a Inovação de Desenvolvimento da FCT
- Norwegian University of Life Sciences (NMBU)
- Politecnico di Milano
- Purdue University
- SciLifeLab
- Technische Universität Dresden (TU Dresden)
- The University of Manchester
- University of East Anglia
- University of Florida
- University of Groningen
- University of Oxford
- University of Texas at El Paso
- University of Warwick
- University of Warwick;
- 25 more »
- « less
-
Field
-
into insight). To tackle this, the research blends ideas from knowledge graphs, stream processing, database theory, logic, edge and cloud computing, and Artificial Intelligence into a single coherent framework
-
, this PhD thesis will focus on the characterization of multipolar electrograms (EGM) through graph signal processing (GSP). The underlying hypothesis is that local propagation patterns in AF are associated
-
of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges
-
thesis in the field of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously
-
properties are proved, refuted by counterexample, or reported as an explainable gap. All of it is written in a domain-specific language whose syntax trees and graphs we keep in a projectional editor (JetBrains
-
knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable
-
models ranging from baseline approaches to graph neural networks. You will also oversee the open release of project datasets, models, code and documentation. The successful candidate will join Oxford's
-
(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
-
graphs and network medicine • Translational data science for therapeutic discovery Primary Responsibilities: The Postdoctoral Research Associate is expected to lead and contribute to independent and
-
develop knowledge-grounded reasoning methods that connect salient nucleotides, motifs, genes and regulatory elements to biological annotations, ontologies, knowledge graphs and literature-derived evidence