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information, for example data derived from remote sensing, use point process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating
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data efficiency. Decision Making/Autonomy (10%) – Lead architectural and data design decisions; prioritize experiments aligned with program milestones; evaluate trade-offs between model accuracy and
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with ecosystem service models and spatial datasets. Key Competencies Strong programming skills (Python/R/JavaScript) for tool and interface development. Ability to implement or learn GeoTOPSIS/VectorMCDA
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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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A crystal in space represents a distinct state of matter, with spatial periodicity in its lattice structure underpinning its band structure and optical properties. This project concerns time
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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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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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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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-wide initiative to build foundation models that simulate the evolution of tumor ecosystems. You will be the lead engineer contributing to large-scale generative modelling on single-cell, spatial-omics