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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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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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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 closed-loop brain–computer interfaces aimed 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
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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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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
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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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with rapid binary-population synthesis codes to detailed stellar evolution calculations with 1D stellar evolution codes. The models you develop will be directly linked to new and upcoming observations
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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