Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- University of Antwerp
- Wageningen University & Research
- Amsterdam UMC
- Inria, the French national research institute for the digital sciences
- Maastricht University (UM)
- NTNU Norwegian University of Science and Technology
- Tilburg University
- University of Exeter
- University of Twente (UT)
- Aalborg Universitet
- Aalborg University
- Academic Europe
- Forschungszentrum Jülich
- Grenoble INP - Institute of Engineering
- Helmholtz-Zentrum Geesthacht
- KU LEUVEN
- 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
- NTNU - Norwegian University of Science and Technology
- Norwegian University of Life Sciences (NMBU)
- Politecnico di Milano
- Purdue University
- RMIT University
- SciLifeLab
- Tallinn University of Technology
- Technische Universität Dresden (TU Dresden)
- The University of Manchester
- Universidad de Alicante
- University of East Anglia
- University of Florida
- University of Groningen
- University of Texas at El Paso
- University of Warwick
- University of Warwick;
- 29 more »
- « less
-
Field
-
qualification. Professional assignment: Chair of Scalable Software Architectures for Data Analytics (Prof. Dr. Michael Färber) Research areas: Natural Language Processing, Large Language Models, Knowledge Graphs
-
Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 27 days ago
Conference on Communication, Control, and Computing 2023. [9] F. De Moor, O. Boullé, D. Lavenier, De Bruijn Graph Partitioning for Scalable and Accurate DNA Storage Processing, BioRxiv, 2025. [10] M. C. Davey
-
project Can AI interpret graphs like a human materials scientist that relates diagrams to composition temperature, pressure, synthesis, processing and uncertainty? The PhD candidate will develop and
-
Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
-
unrealistic, and can therefore result in inadequate models. This project proposes a generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able
-
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
-
) Developing deep learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level
-
as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the project Can AI interpret graphs like a human materials scientist
-
human activities that push species to extinction and potentially disrupts ecosystem functionality. Our interdisciplinary lab will develop novel Graph Representation Learning models to understand and