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Field
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the transmission of emerging respiratory infections in highly heterogeneous populations. As part of this, the impact of the heterogeneity in the structure of the individuals contact network on disease transmission
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teaching and knowledge dissemination. The PhD position is one of 13 PhD positions in the Marie Skłodowska Curie Action Doctoral Network Scheme TRANSFORM. TRANSFORM focuses on examining whether and to what
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Bayesian models, and an interest in learning Hierarchical Modelling of Species Communities (HMSCs). · Fieldwork Experience: Proven ability to collect and analyze ecological data, especially in boreal
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using copulas [4] as in e.g. [2]. A longer-term objective is to study the extension of the approach to the analysis of Bayesian neural networks, where the weights and bias of the network are also defined
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statistical modeling techniques, particularly Bayesian models, and an interest in learning Hierarchical Modelling of Species Communities (HMSCs). · Fieldwork Experience: Proven ability to collect and
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mixed-effect models, overparameterized regression, Bayesian models and regularization. On the biological side, some knowledge of crop physiology, plant breeding and quantitative genetics will be useful
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quantum forces that govern them. You will join a creative and collaborative research group with a strong international position and network of international collaborators. Together, we strive to foster
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-informed neural networks, and Bayesian approaches to address evolving weather patterns in both spatial and temporal dimensions. Enhance ML Predictability: Maintain model predictability under data
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learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural language processing, algorithmic learning, fault
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the project and the program. Opportunities to participate in conferences, symposia, and networking events to share and enhance your research. Your role will be pivotal in driving innovation and contributing