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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
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project focuses on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High
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and signal processing, machine learning is now a global field supported by major international conferences such as AISTATS, NeurIPS, ICML, and MVML. Applying these methods to the study of Paleolithic
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the suitability and potential benefits of LLMs (and other machine learning models) in these tasks. These investigations include the feasibility, practicality and success evaluation of prototype implementations
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Danish health registry data, you will work with spatial data, such as disease maps and medical imaging. Such data are highly informative but also pose significant privacy risks. Your work will focus