PhD Candidate: Generative Diffusion Modelling and Associative Memory at the Donders Centre for Cognition

1 maand geleden


Nijmegen, Nederland Radboud University Voltijd

As a PhD candidate, your main responsibility will be to work on research projects in generative machine learning. These projects require both training of deep neural networks on large datasets and performing theoretical analyses and calculations to elucidate their properties. In particular, you will become proficient in training and analysing state-of-the-art diffusion models and variants such as the newly introduced consistency models. These models rest on a joint foundation of deep learning architectural research and mathematical theory. They therefore present an exciting research area for theoretically minded individuals who are also interested in the practice and applicability of state-of-the-art generative modelling.

Your research will focus on the interconnections between generalisation and memorisation in generative models. The aim is to understand the ‘magic’ of generative diffusion models and cast light on the computational basis of our own human memory as well. Depending on the specific project, you will collaborate both with statistical physicists and with experimental and theoretical neuroscientists, such as Dr Freyja Ólafsdóttir (DCN).

As part of your duties (10%) you will assist in teaching courses in machine learning and mathematics for Bachelor’s and Master’s students. You will also co-supervise ambitious Master’s students with their research projects.



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