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learning, knowledge exchange, and joint knowledge creation. During these stays, 100% of the time of the PhD is dedicated to the research program. As part of NGinfra, the PhD candidate will also have ample
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applications and machine learning. Good analytical skills and a positive attitude towards interdisciplinary work. We offer you, following the Collective Labour Agreement for Dutch Universities: A salary of
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for computationally demanding applications and machine learning. - Good analytical skills and a positive attitude towards interdisciplinary work. Additional Information Benefits We offer you, following the Collective
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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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: 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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artificial intelligence, computational cognitive science, human-computer interaction, computer science, information systems, or another relevant field. - A keen interest in pursuing interdisciplinary research
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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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on logic to precisely describe the system in question and check desired properties, with both academia and industry recognising its importance. Distribution is an integral part of innumerous computer systems
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check desired properties, with both academia and industry recognising its importance. Distribution is an integral part of innumerous computer systems, providing them with essential improvements to aspects