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Field
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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that activity-silent mechanisms, such as short-term synaptic plasticity, also play an important role. We will experimentally target these two mechanisms, using EEG in combination with machine learning to reveal
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase
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University. Requirements A master’s degree in (applied) mathematics (or related), with a strong background in computational methods, preferably also using computational frameworks for machine learning in
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networks, or network science, and relevant background knowledge n methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods. Examples