-
learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
-
learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
-
, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong
-
Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation Commitment to Diversity The University