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are suitable. The aims of this project are to Review operating characteristics proposed for rare disease trials Develop novel Bayesian operating characteristics for different types of rare disease trials Apply
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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(written and spoken) The successful candidate is expected to move to Bergen Desired Qualifications: Experience with Stata Experience with causal inference methods, survey design, and/or machine learning
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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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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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, 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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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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. 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