Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research
-
France 91120, France [map ] Subject Areas: Applied Mathematics - statistical learning, graph learning or large language models Appl Deadline: 2026/03/24 03:59 AM (posted 2026/02/03 05:00 AM, listed until
-
education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
-
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
-
some overlapping measures in the individual data sets and through the use of advanced analytic tools including machine learning and graph theoretics, one can discover multiple developmental pathways in
-
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
-
-Neria Logic, set theory Shai Evra Graph theory, representation theory, number theory Adi Glucksam Complex analysis, potential theory, and dynamics Or Hershkovits Geometric analysis Mike
-
Danish biomedical research institute, and a part of the University of Copenhagen, a highly-ranking European university. More information about the group is given on the lab website https
-
Infrastructure? No Offer Description Mission: Optimize graph databases, including research, implementation, experimentation and dissemination tasks. Functions to be developed: Analyze the state of the art in
-
Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 6 days ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector