Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- Aalborg University
- Leibniz
- University of Twente
- Inria, the French national research institute for the digital sciences
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- CNRS
- Delft University of Technology (TU Delft)
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich
- Technical University of Munich
- Umeå University
- University of Luxembourg
- Vrije Universiteit Brussel (VUB)
- Centrale Supelec
- Constructor University Bremen gGmbH
- Cranfield University
- Eindhoven University of Technology (TU/e)
- Empa
- Fundació per a la Universitat Oberta de Catalunya
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- KU LEUVEN
- LEM3
- Leipzig University •
- Linköping University
- Molde University College
- Monash University
- Newcastle University
- Queensland University of Technology
- SciLifeLab
- Stockholms universitet
- Swedish University of Agricultural Sciences
- The University of Chicago
- UNIVERSITY OF VIENNA
- University of Amsterdam (UvA)
- University of Birmingham;
- University of Exeter
- University of Greenwich
- University of Liège
- University of Nottingham
- University of Southern Denmark
- University of Twente (UT)
- University of Vienna
- University of Warwick
- Universität Wien
- Université Toulouse Capitole
- Utrecht University
- Vrije Universiteit Brussel
- Wageningen University & Research
- cnrs
- 40 more »
- « less
-
Field
-
learning-powered algorithms as well as hybrid approaches, combining either reinforcement learning or deep learning (Graph Neural Networks) with human-based modelling, for fully flawless and autonomous method
-
relevant entities, relations and events from health records and organise them into knowledge graphs, supported by techniques for efficient retrieval and analytics. As a first application, AI:HealthData Lab
-
explainable AI (XAI) methods with user-centred interaction design, combine machine learning with alternative AI methodologies (e.g., rule-based reasoning, knowledge graphs, hybrid approaches where relevant
-
the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration
-
://www.yantlab.net/ ) Using large-scale graph-based pangenomics and forward evolutionary simulations, the student will develop predictive models of polyploid genome evolution across contrasting timescales. The student
-
of such technology. This work will be developed with the support of the interplay of Semantic Technologies (e.g., ontologies, knowledge graphs) and Artificial Intelligence. Moreover, domain explanation requirements
-
of such technology. This work will be developed with the support of the interplay of Semantic Technologies (e.g., ontologies, knowledge graphs) and Artificial Intelligence. Moreover, domain explanation requirements
-
. You would be welcomed in the the Yant Lab (https://www.yantlab.net/ ) Using large-scale graph-based pangenomics and forward evolutionary simulations, the student will develop predictive models
-
external funding to cover international fees. References: R. Fedorov, A. Nihei, G. Gryn’ova, Multi-Solvent Graph Neural Network for Reduction Potential Prediction across the Chemical Space, J. Chem. Inf
-
Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 13 days ago
the desired geometric capabilities within the model. In a second phase, we want to examine potential complementarity between MLLMs and scene graphs built from images to combine localization methods with