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The Center for Quantitative Genetics and Genomics (QGG) at Aarhus University, Denmark, invites applications for a two-year postdoctoral position in plant regulatory genomics and artificial
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computational pipelines for QTL data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web
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machine learning for plant biology Employer Aarhus University (AU) Location Aarhus, Midtjylland (DK) Salary Competitive Closing date 12 Oct 2026 View more categories View less categories Discipline
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | 10 days agointegration of computational genomics, machine learning, cancer genetics, and translational cancer biology. We are housed within the UPMC Hillman Cancer Center and the Departments of Pathology and
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position is based at the Center for Quantitative Genetics and Genomics (QGG) under the supervision of Asst. Prof. Irene Julca. The position is offered as a two-year appointment, with the possibility of
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environment, in which the candidate will be able to exchange across research fields (machine learning, bioinformatics, biology, and quantitative genetics) and applications (plant biology, agriculture
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candidate will join the Integrative Cell Signalling group led by Prof. Dr. Alexander Skupin, which focuses on multiscale biology of neurodegeneration by combining multiomics and imaging characterization of
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EN FR Go back Scroll to top Share Postdoc position in Machine Learning for Integrative Genomics (PhD) Impact on presbycusis of repeated acoustic trauma © Charles DAUGUET, Institut Pasteur HIV
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) and the integrative modeling led by the graduate student. Apply machine-learning and AI-assisted tools in genome analysis, population and disease-ecology work, and pipeline development, with attention
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) and the integrative modeling led by the graduate student. Apply machine-learning and AI-assisted tools in genome analysis, population and disease-ecology work, and pipeline development, with attention