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of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable
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Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a focus on
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, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity binders that engage with therapeutic targets or efficient (bio
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demanding applications as in environmental and exposure monitoring, and the green hydrogen industry? If so, then you have a part to play as a PhD candidate in our research team. Put your ideas to the test at
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networks, or network science, and relevant background knowledge on methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods
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(+/–) applied to the electrodes can be optimised in a machine-learning approach to optimise the gel morphology and the embedded stimuli-transduction pathways towards the targeted mechanical behaviour. Departing
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to collect large-scale experimental datasets and apply machine learning techniques to predict the performance of complex food ingredients, supporting the creation of products with lower environmental
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to data analysis and science communication. PhD students at BirdEyes will gain expertise in cutting-edge ecological monitoring techniques, big data analysis, and creative science communication. The centre
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-edge ecological monitoring techniques, big data analysis, and creative science communication. The centre provides access to extensive global datasets, fostering collaboration across disciplines and
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. Research methods include computational modelling, brain imaging (fMRI), machine learning, behavioural methods, and other techniques. Virtually everything we sense, think and do is uncertain. For instance