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motivated PhD candidate to conduct research in the field of evidence synthesis. The project will focus on statistical methods for living network meta-analyses – dynamic networks of interventions
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or numerical methods in the context of environmental research is highly advantageous. Experience with the interpretation of field data is advantageous. Grade requirements: The norm is as follows: the average
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published many high impact papers in top scientific journals such as Science, PNAS, Phys. Rev. Lett., etc. Our approach is highly interdisciplinary, using emerging state-of-the-art methods from mathematics
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. The project will focus on statistical methods for living network meta-analyses – dynamic networks of interventions that are continuously updated as new evidence emerges. This PhD position offers a unique
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. Contribute to developing new models, techniques and methods Undertake management/administration arising from research Provide guidance, as required, to support staff and any students who may be assisting with
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or numerical methods in the context of environmental research is highly advantageous. Experience with the interpretation of field data is advantageous. Grade requirements: The norm is as follows: the average
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal