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students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website
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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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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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This doctoral project aims to improve our understanding of chemical contaminants in food, focusing on detecting both known and previously unidentified compounds using suspect and non-target screening approaches
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societal objectives. The program recognises the growing interconnectedness of sustainability, sustainable finance, and AI, as FsF are increasingly required to redirect capital towards sustainable investments
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students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website . At the Division of Systems and Control, we develop
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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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ecology, biodiversity monitoring, and molecular methods. The project explores how airborne environmental DNA (eDNA) can be used to detect and monitor migratory species, invasive species, and pathogens