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for probabilistic unsupervised learning for structured biological data. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/307053/3-years-phd-position-in-probabilistic-machine
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welcomed. The project sits at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development. Tasks of the PhD Student - Develop and
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data combined with focussed innovation in statistical and computational methods including machine learning to advance our understanding, treatment, and prevention of human disease. Information
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application! We are now looking for 1–2 PhD students for the Division of Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Within the research unit
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machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification or regression models); familiarity with large language models (LLMs) and other
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, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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and analysis of dedicated algorithms for training analog circuits directly from data. In this PhD project, you will develop a novel system-theoretic framework for learning in analog circuits and
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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates