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Field
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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Candidate We seek an ambitious researcher with a strong foundation in Bayesian theory, machine learning, and mathematical modelling. You should be confident in software development and capable of passing a
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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
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CRISPR tools to correct or model the defective genomic DNA, and further deliver these newly-engineered editors into targeted organs to correct the corresponding phenotypes. The current disease models
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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to recruit a Postdoctoral Research Fellow for the project titled “A Targeted Support System to Bridge the Advice Gap,” sponsored by UKFin+ (EPSRC), to work jointly with Raffaella Calabrese and Fidelity
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management. Experience of undertaking Bayesian spatial statistical analyses. Experience working in international context or with international collaborators. Excellent written and verbal communication skills
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, epidemiology. Strong mathematical and quantitative skills. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not
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can be tackled. A video describing the project can be viewed here: https://www.youtube.com/watch?v=IzPuuBnrIDc . The successful candidate will be developing Bayesian models for estimating
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond