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community of Digital Research Engineers. The offices also host computer infrastructure and machine learning/data science/research data management experts, who develop, build, and manage the local and national
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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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biology and genetics. Experience using Git for version control. Ability to quickly learn new skills. Experience working in a Unix/Linux environment. Excellent verbal and written English skills. We
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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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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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modeling, machine learning, and AI techniques applied to biomedical data is a plus. Clinical Proteomics: Experience with clinical trial data, real-world evidence (RWE), and biomarker-driven trial designs is
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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
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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