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lines and primary cells to evaluate genome editing efficiency. 4. Analyze editing outcomes using Next Generation Sequencing (NGS) and other molecular tools. 5. Mentor research assistants, graduate
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development and regeneration. Our research combines in vivo genetic models, in vitro organoid systems, advanced imaging, and high-dimensional sequencing approaches to uncover fundamental principles by which
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presented before you can take up the position. Experience in transcriptomics analyses, including single cell RNA sequencing analyses Experience in multi-omics data integration Experience in statistical
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sequencing and spatial transcriptomics data. Learn and master skills for integrative analysis of high-dimensional bulk and single-cell multi-omics data. Gain rich knowledge in cancer biology, cancer genomics
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
, with opportunities to supervise honours and graduate research students. The role provides the chance to develop a strong research agenda aligned with PSC’s areas of specialisation, including
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, interdisciplinary team working at the intersection of data innovation and sustainability science, aligned with the values and principles of integral ecology. Job Description IMPORTANT INFORMATION - PLEASE READ
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structural biology to tackle challenging scientific questions. Your responsibilities will include, but are not limited to: Multi-omics analysis of bulk and single-cell sequencing data. Developing deep learning
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, molecular biology, or related disciplines. Additional Qualifications Considered Research experience in genetics, next generation sequencing, bioinformatics, biophysics, physics, math and engineering may also
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
in artificial intelligence (AI) for settings involving multiple interacting decision-makers---whether autonomous AI agents, humans, or a combination of both. Applications include mixed-autonomy
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as