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Field
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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available (>1.1 million people). The goal is to establish how many archaic human groups contributed to our genomes. Your task is to infer key parameters of the archaic human evolutionary history such as
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, and eager to apply computational skills to cutting-edge biological questions. In this project, you will develop a tool to infer karyotypes from individual cells based on their transcriptome, and use
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. This will involve investigating techniques for model compression and efficient inference to enable on-board condition monitoring directly at the wind turbine, reducing data transmission requirements, central
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. Responsibility: * Develop or integrate novel statistical methods and algorithms for analyzing large-scale -omics data, including gene regulatory network inference, cell lineage reconstruction, multi-dimensional
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under varying lighting, fabric blends, and soiling; (5) porting the inference pipeline to an embedded/edge-compute platform; (6) integrating with our robotic pick-and-place cell for iterative field trials
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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& Analysis Perform quality control, alignment, and quantification of bulk and single-nucleus RNA-seq datasets. Conduct differential expression, clustering, trajectory inference, cell type annotation, and
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems