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located across 5 European countries. MonaLisa is at the forefront of artificial molecular machine research, setting the stage for breakthroughs in chemical synthesis, nanotechnology, medical treatment and
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amounts of language produced by adults or children, we approach the question by employing computational models that learn by finding patterns in language data. Can they learn the different modal meanings
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computational models that learn by finding patterns in language data. Can they learn the different modal meanings simply by paying attention to what they “hear”? Or do they need to come prepared with certain
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systems—commonly referred to as neuromorphic computing—holds the potential to create highly intelligent machines capable of supporting a wide range of everyday applications, from autonomous vehicles
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systems—commonly referred to as neuromorphic computing—holds the potential to create highly intelligent machines capable of supporting a wide range of everyday applications, from autonomous vehicles
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systems—commonly referred to as neuromorphic computing—holds the potential to create highly intelligent machines capable of supporting a wide range of everyday applications, from autonomous vehicles
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systems—commonly referred to as neuromorphic computing—holds the potential to create highly intelligent machines capable of supporting a wide range of everyday applications, from autonomous vehicles
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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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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic modelling of materials and machine learning. Experience in atomistic modelling (molecular