Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of Oslo
- ;
- AbbVie
- Harvard University
- Korea Institute for Advanced Study
- Nanyang Technological University
- University of British Columbia
- Wayne State University
- Auburn University
- Genentech
- LINGNAN UNIVERSITY
- Marquette University
- NTNU - Norwegian University of Science and Technology
- Nature Careers
- Northeastern University
- RMIT UNIVERSITY
- RMIT University
- The University of Alabama
- The University of Queensland
- The University of Western Australia
- University of Alabama, Tuscaloosa
- University of Bergen
- University of Liverpool
- University of San Francisco
- University of Texas at Austin
- University of Waterloo
- Western Norway University of Applied Sciences
- Zintellect
- 18 more »
- « less
-
Field
-
available for two years. Keywords: Geometric Deep Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial
-
Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation Commitment to Diversity The University
-
Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation. Commitment to Diversity The University
-
background in systems thinking, analysis and modelling Experience in teaching and supervision in higher education at least on MSc level Knowledge of graph theoretical approaches and graph signal processing
-
, and clinical safety datasets Implement graph-based retrieval-augmented generation (RAG) methods to enhance knowledge extraction and information synthesis Develop cross-pathway analytical methods using
-
Condensed Matter Physics and Materials Sciences o Theoretical and Computational Biophysics o Soft Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal Graph
-
, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation
-
written and verbal communication skills regarding research results. Preferred Qualifications: Experience with deep/graph neural networks and active involvement in data science and machine learning projects
-
position focuses on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and
-
on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and reinforcement learning