Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- NEW YORK UNIVERSITY ABU DHABI
- Nature Careers
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- University of Luxembourg
- Technical University of Denmark
- The University of Arizona
- University of Washington
- CNRS
- KINGS COLLEGE LONDON
- New York University
- Princeton University
- Purdue University
- Texas A&M University
- University of North Carolina at Chapel Hill
- University of Southern Denmark
- University of Texas at Arlington
- Wayne State University
- ;
- Aix-Marseille University
- Aix-Marseille Université
- Bar Ilan University
- Bilkent University
- DURHAM UNIVERSITY
- Delft University of Technology (TU Delft); yesterday published
- Durham University
- EPFL - Ecole Polytechnique Fédérale de Lausanne
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); 27 Sep ’25 published
- Georgia State University
- ICN2
- Indiana University
- Jagiellonian University
- KTH Royal Institute of Technology
- Karolinska Institutet (KI)
- Leibniz
- New York University of Abu Dhabi
- Northumbria University;
- Pennsylvania State University
- THE UNIVERSITY OF HONG KONG
- Technical University of Munich
- Technische Universität Berlin
- The Hebrew University of Jerusalem
- The Ohio State University
- UNIVERSITE PARIS CITE
- UNIVERSITY OF SYDNEY
- UNIVERSITY OF VIENNA
- Umeå University
- University of Cambridge
- University of Central Florida
- University of Florida
- University of Groningen
- University of Kansas Medical Center
- University of Nevada, Reno
- University of Sydney
- University of Virginia
- University of Zurich
- Virginia Tech
- 47 more »
- « less
-
Field
-
learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
-
collaboration with Growgraph, an R&D startup engaged in advancing knowledge graph technologies and AI-driven methods for structured data analysis. This partnership aims to foster the transfer of research outcomes
-
and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
-
. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
-
: extremal graph theory, Ramsey theory, probabilistic combinatorics. • Candidates should have (or be near completion of) a PhD in mathematics. • Candidates should have a strong research record in Combinatorics
-
use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
-
contribute to the development of a proof of concept obtained at University Côte d’Azur for accessing the content of a metabolomics knowledge graph (KG) with a large language model. It is Python prototype of a
-
on processing and developing representation models for diverse data sources, including time-series data (EEG, video, mass spectrometry) and chemical data (molecular graphs, SMILES strings) related to odorant
-
Michael Bronstein, AITHYRA Scientific Director AI and Honorary Professor of the Technical University of Vienna in collaboration with Ismail Ilkan Ceylan, expert in graph machine learning, invites
-
dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a Ph.D. and have exceptional research potential. Teaching may be