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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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(HPC/parallel environments), and open/reproducible release of data and analysis scripts under FAIR principles. Dissemination through high-impact journal publications and conference presentations in solar
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, with a particular focus on nutrient and metabolite uptake by developing embryos. In parallel, the project will apply our previously developed low-input detection methods to characterize intracellular
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multiome RNA-seq, ATAC-seq and massively parallel reporter assays (MPRAs) for unbiased genome-wide analysis for understanding the phenotypic plasticity in different cancer cell states. Work tasks The work
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to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data
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philosophy to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and
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the modeling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data sources, and