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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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challenges, and we are currently moving the code to a new python based High Performance Computing enabled modelling framework. This is an exciting opportunity to contribute to a high-impact scientific codebase
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the role of toe erosion in triggering landslides in sensitive clays. The focus will be on developing computational models that will quantify the erosion mechanisms, precursors and the time to failure
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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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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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computational facilities for testing & modelling natural clays and access to data on natural slopes in Western Coast of Sweden. These state-of-the-art resources empower you to conduct cutting-edge research with
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cross-layer defenses that ensure secure and efficient AI model development at scale. Information about the division The department of Computer Science and Engineering is strongly international, with
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Computational Arts, Music, and Games within the DSAI division. About the research project This position is related to investigating learned cultural representations in data search spaces of generative AI models
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design. One of our group's goals is to create efficient surrogate models that reduce the computational cost of MD simulations by several orders of magnitude. Notable examples of our work in this area
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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