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and propagate uncertainty from image features to predicted PEMWE behavior. Bayesian experimental design and process optimization. The digital twin will form the basis for Bayesian optimization
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: Bayesian hierarchical deconvolution of spatial bins using matched snRNA-seq reference, cell-cell communication inference, and spatial niche identification Multi-omics integration: linking spatial and single
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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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. 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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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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. The overall objective is to contribute to next-generation decision support and precision health. At BTH, the PhD project will focus on computational and methodological development, with particular emphasis
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new data becomes available, while preserving previously acquired knowledge. A key objective is to design signal representations and learning mechanisms that enable stable adaptation without forgetting
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are looking for a colleague who will advance our genomic research on horticultural crops. The overall objective of this position is to develop research concepts and tools within the Swedish horticultural