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advanced detection techniques, analyse data, and carry out numerical trajectory simulations of deceleration and scattering processes. You will have the opportunity to work with state-of-the-art and world
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candidate, you will investigate the learning capabilities of feed-forward and recurrent models of neural circuits with various degrees of biological plausibility, with a focus on: Transferability of
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experiments, design and implement advanced detection techniques, analyse data, and carry out numerical trajectory simulations of deceleration and scattering processes. You will have the opportunity to work with
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key physical properties of mixtures of molecules, including solubility, viscosity, and surface or interfacial tension. We will train a range of AI models to allow us to predict these properties from
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of core web technologies. You have solid experience in Python and JavaScript programming languages. You preferably have experience with building and using machine learning models. You care deeply about
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the learning capabilities of feed-forward and recurrent models of neural circuits with various degrees of biological plausibility, with a focus on: Transferability of representations in multi-task settings
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at restoring motor function in models of motor disorder. Some part of the role may involve teaching and up to 0.1 FTE of your time may entail teaching activities. A fully funded PhD position is available
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of semantics, pragmatics, philosophy of language, conversational analysis and dialogue modelling), the series of experiments will result in four journal papers, to be integrated into a dissertation. You will
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, viscosity, and surface or interfacial tension. We will train a range of AI models to allow us to predict these properties from the chemical structure alone. Once established, we will expand the self-driving
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machine learning models. You care deeply about privacy and are keen to create. You have a good command of spoken and written English. You are motivated to publish at top-tier academic venues. What we offer