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Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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editing, mouse modeling, iPSC disease modelling, cell sorting, live cell imaging, spatial and single cell omics modalities, and advanced image processing and analysis, amongst a variety of other specific
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analyses of public and confidential datasets relevant to the funded projects. Responsibilities include applying causal inference, spatial econometric, and multilevel modeling techniques to examine the
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ACCE+ DLA Programme: Assessing the Future Effectiveness of Spatial Conservation Networks in tetrapods School of Biosciences PhD Research Project Competition Funded Students Worldwide Prof Gavin
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development. It will involve a paradigm shift which combines geo-spatial-temporal modelling, prospective life cycle analysis, techno-economic assessment and AI methodologies. It will map and analyse
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and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173-189. [2] Belbeze, S., 2020. http://infoterre.brgm.fr/rapports//RP-70282-FR.pdf [3] Meusburger, K
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resolution (such as MEG or intracranial EEG) may also be considered, in combination with behavioral recordings and computational modeling. Where to apply Website https://www.unitn.it/ Requirements Additional
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and transcriptomics. Analyzes spatial transcriptomic datasets to uncover tissue organization and disease states. Develops integrative models combining genomics with pathology images or time-series data