Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Harvard University
- Nanyang Technological University
- University of British Columbia
- University of Oslo
- ;
- Francis Crick Institute
- INESC TEC
- Institute for Basic Science
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- Marquette University
- Middlesex University
- Middlesex University;
- NTNU - Norwegian University of Science and Technology
- National University of Singapore
- Northeastern University
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen
- Singapore University of Technology & Design
- University of Bergen
- University of Michigan
- University of Science and Technology of China
- University of Texas at Austin
- University of Waterloo
- Wayne State University
- 13 more »
- « less
-
Field
-
://dimag.ibs.re.kr/ Successful candidates for the research fellowship positions will be new or recent PhDs with outstanding research potential in all fields of Discrete Mathematics with emphasis on Structural Graph
-
, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
-
, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
-
, such as InfiniBand and Ultra Ethernet. Our project will also deliver a comprehensive set of PyTorch libraries, encompassing various optimized models for scientific applications, including Graph Neural
-
Skills: Proficiency in Python, TensorFlow, PyTorch, or similar frameworks. MS or higher degree (PhD preferred) degree in Computational Neuroscience, Computer Science, Bioinformatics, or a related field
-
, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
-
trials, and treatment guidelines, and transform these into structured knowledge graphs encoding relationships among histotypes, biomarkers, therapies, and outcomes. Assess the accuracy, completeness, and
-
extreme weather to cybersecurity threats. Working within the LDTRC, you will undertake a range of research tasks, including: 1) Defining the ontology and knowledge graph architecture for a scalable digital
-
apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
-
extreme weather to cybersecurity threats. Working within the LDTRC, you will undertake a range of research tasks, including: 1) Defining the ontology and knowledge graph architecture for a scalable digital