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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected to the project are: PhD Student Position in Generative
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human
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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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student, you will be supported by a multidisciplinary team with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected
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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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machine learning, computer vision, and materials science. The focus of this position is on development of neuro-symbolic models for the effective behaviour of the complex microstructure of recycled
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to begin focused research early on. To complement their background, optional training in natural language processing, machine learning, and linguistics will be available. During the first year, the student