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Field
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data using descriptive statistics, visualization, mapping, and advanced statistical methods including Bayesian hierarchical regression, advanced difference-in-difference techniques for causal inference
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some of today’s most pressing societal challenges, including biodiversity loss, climate change, and environmental pollution. Our research combines advanced spatio-temporal modelling, Bayesian inference
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, health and medicine, logistics, infrastructure, and industrial operations. The PhD project will focus on the intersection of machine learning, causal inference, and classical statistical methods. A central
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department by carrying out both quantum information and computation projects ranging from quantum device characterization, error mitigation/suppression/correction, Bayesian-inference-based quantum information
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
gradient methods for high-dimensional neural networks, reinforcement learning, variational inference). In addition to methods development and theoretical research on modern AI and ML methods, the successful
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor
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qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage. Experience with land-surface models
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advanced statistical methodologies, including several of the following: Survival analysis Hierarchical and mixed-effects models Clinical trial design and analysis Structural equation modeling Bayesian data