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: The main objective of this PhD project is to use a globally unique and most extensive (1974 onwards) airborne environmental DNA (air eDNA) dataset from Sweden, to test if the air-eDNA method can be used as
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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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orogenic stages, leading to eventual LCT pegmatite formation during late-stage orogenesis. The overall objective of this PhD project is to track the geochemical cycling and behaviour of lithium in
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are of interest. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial
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are of interest. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial
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for hypergraphs and partially ordered sets (POSets), funded by the Swedish Research Council. This project is concerned with saturation problems for two classes of combinatorial objects: hypergraphs and posets