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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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The UKRI-funded 5-year project, 'Colombo: Layered Histories in the Global South City', selected for funding by the European Research Council under its 'HORIZON' programme, is recruiting to its
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The UKRI-funded 5-year project, 'Colombo: Layered Histories in the Global South City', selected for funding by the European Research Council under its 'HORIZON' programme, is recruiting to its
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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You will lead the qualitative work to understand how individuals experienced the intervention and app how it worked. This will directly impact the design and refinement of the intervention, and so
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Services staff as requested and appropriate Engage in appropriate use of non-staff budget: travel costs, IT, office costs etc. You will make a full and active contribution to the principles of the 'Sheffield
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
was pivotal in identifying LMTK3's oncogenic roles, including its contribution to chemo- and endocrine resistance, and its function as a regulator of the tumour microenvironment. His group successfully solved
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used to measure motion and deformation. It provides comprehensive full-field deformation data, essential for analysing complex materials, structures and model validation. The DIC community has developed
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dominant source of errors (Agarwal et al., Quant. Sci. Technol. 2024), so that for any verification approach to be useful it needs to go beyond widely employed Markovianity assumptions. This work, directly