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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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approach. Furthermore, you should be able to place your own research in a broader context and relate your work to the overall objectives of the project and the subject area. You work in a structured manner
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. While the project’s tasks and objectives are defined, you are encouraged to develop your own research questions and methodological approaches. For more information visit MiningBrines Duties You will
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period marked by shifting EU politics, geopolitical uncertainty and climate change. The project investigates how forest governance is shaped by tensions between environmental objectives, economic interests
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equitable teaching. The graduate school specialises in practice-based mathematics teacher education. This includes examining how teachers’ work is made into a learning objective in teacher education, and how
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, particular emphasis will be placed on the applicant’s ability to successfully complete the doctoral programme and to contribute to the project’s focus and objectives. Assessment will take into account the