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Field
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The project will include two secondments (6 months each): Stuttgart University (USTUTT): formulation of n- and p-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will
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transparent and intelligible. Although explainable AI methods can shed some light on the inner workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and
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AI architectures such as Transformers, large language models (LLMs), vision-language models, recurrent neural networks (RNNs), and related techniques. Mathematical maturity: A solid grasp of key
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of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable
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approaches that build processes by mutation operators [1], natural language processing techniques with recurrent short-term memory (LSTM) neural networks [2], and variational autoencoders (VAE
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workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing
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of this annex, as well as to: Programming in Python and R. Statistical classification and machine learning methods: SVM, neural networks and logistic regression. 3.2. Qualification: Official Master’s degree in
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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in working memory PHD 3: The dynamic interplay between brain and bodily rhythms in
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are recruiting three PhD students with distinct research foci: PHD 1: The functional role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in