25 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Brunel University London" Fellowship positions at City of Hope
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Molecular Dynamics methods and experience in code development is preferred. City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location. City of Hope is
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highly motivated postdoctoral fellow to study transcriptional regulation and RNA metabolism in normal development and human diseases, such as neurological disorder, immunology, and cancer. In particular
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publications and scientific presentations and engage in lab meetings, seminars, and professional development activities. Your qualifications should include: · Candidates must hold an MD or PhD
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evolution. Our work builds on experience developing pangenome graph construction and analysis tools (PGGB, ODGI, IMPG) and contributions to the Telomere-to-Telomere Consortium and Human Pangenome Reference
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of Hope. This role offers an exciting opportunity to investigate immune checkpoint signaling within the tumor microenvironment and contribute to the development of novel anticancer therapies
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. This project involves the development and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in the dynamics of G-protein coupled receptors to enable
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-seq and next generation long reads sequencing from Oxford Nanopore. · Participate in the development of new computational methods for downstream analysis of genomics data and multiomics
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failure from a developmental perspective. We are interested in the dynamics of beta-cell heterogeneity, islet organization and cell-cell interaction in fetal and neonatal development. The Dhawan lab
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to vaccine development utilizing viral vectors to contribute to an effort in developing neoantigen expressing vaccines for clinical investigations. The position is available as part of a multi-disciplinary
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Institute at City of Hope. You will work on projects related to the molecular mechanisms underlying the aging-related diabetes development using genetically engineered mouse models and multiple omics