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consisting of senior researchers and 4 different PhD candidates will investigate the Southern Norwegian North Sea source-to-seep system through analysis of new and existing data from four different
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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qualifications: Experience with implementation or applications of large machine learning models Experience with generative methods for protein design and/or docking simulations or generative methods
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses
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results in foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health rec-ords. The work will be done in
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large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing their own research ideas, applicants should be capable
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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
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, or other neurodegenerative disorders. Experience with machine learning for large datasets. Experience with computational methods and workflows for handling large-scale data. Personal skills Highly organized
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data from live imaging and spatially resolved gene expression profiling. The work of the PhD fellow will be theoretical and computational in nature and will include: Developing minimal active-matter
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed