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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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the department’s activities within method development for machine learning-based computational biology. The duties include supervision of PhD students and postdocs, and teaching at basic, advanced and research level
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, the following are required: – Documented several years of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks – Documented several
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focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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at Sahlgrenska Academy of relevance include genomics, metagenomics, culturomics, proteomics, transcriptomics, software development, machine learning, and other statistical analyses of large-scale health data
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study