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. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods to compare deep neural network and other artificial representations
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. The research work will focus on enhancing GNSS localization under interference scenarios using 3D Geographical Information Systems (GIS) data; possible methods include, but are not limited to: Bayesian
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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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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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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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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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interdisciplinary research & research methods Strong analytical skills and a collaborative mindset Experience with (Bayesian) statistics, deep neural network models, Kernel Methods, or data science Experience with
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to staff position within a Research Infrastructure? No Offer Description Do you want to contribute to extending, applying, and disseminating flexible methodologies from the Bayesian machine learning
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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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. 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 to a transformative approach to