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Scholar (FP01) – AI Imaging & Retinal Disease who is interested in ophthalmology to assist in refinement of AI models as well as validation of new biomarkers and data analysis to be able to predict whether
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refines algorithms and workflows for crop and pasture monitoring, modeling, prediction, and decision support and automation; Supervises graduate research assistants and student interns working
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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate ). We also began exploring how
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rigorous quantitative description of phenomena predicted by theories such as K41 and Onsager, which still lack a full mathematical justification. The researcher will work on linear advection–diffusion models
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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predictive models, and interpreting large environmental datasets, collaborating in interdisciplinary projects and in the production of scientific publications. In the performance of duties, it may sometimes be
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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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robust descriptors (e.g., water activity, sorption, glass transition temperature, plasticization, porosity, internal distribution) and provide predictive guidelines to rationally select and design drying