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research project “Is the brain network involved in sentence comprehension replicable and robust?”, led by Dr. Jurriaan Witteman. The project will investigate the neural mechanisms underlying sentence-level
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performance and/or neural structure/function that govern the rate of cognitive development in childhood and/or old age. The post holder will have an interest in theory-based developmental cognitive (neuro
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, or materials informatics. Familiarity with explainable AI or counterfactual explanation methods. Experience with molecular dynamics data, graph neural networks, or multi-component system modelling. Track record
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skills: Good knowledge of ML/AI based techniques to develop fast surrogates (deep neural networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding