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哥伦比亚大学计算生物学与生物信息学博士后招聘

2020年01月06日
来源:知识人网整理
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摘要:哥伦比亚大学计算生物学与生物信息学博士后招聘

  哥伦比亚大学计算生物学与生物信息学博士后招聘

  The Picard Lab at the Columbia University Irving Medical Center (CUIMC) is seeking a motivated postdoctoral researcher with a computational biology and bioinformatics background to join the team. The successful candidate will be responsible for leading computational and bioinformatics research projects related to mitochondrial psychobiology and aging. Our goal is to use computational and network-based approaches to build predictive models linking cellular energetics/mitochondria to higher-level clinical, phenotypic, and age-related processes.

  The successful candidate is expected to develop and execute integrative analyses of clinical and functional genomic data generated in-house from our cellular lifespan model of accelerated aging. Multi-omic datasets with opportunities for independent validation are available. The candidate should have doctoral training or equivalent experience in bioinformatics, together with a background in computational biology, machine learning, network science, and/or computational neuroscience. Prior experience with epigenomics (e.g. DNA methylation), transcriptomics (RNA-seq), and proteomics are desirable.

  Competence with open source bioinformatic and statistical tools in R is highly advantageous. The candidate will be integral part of a cross-disciplinary and translational team of faculty, postdocs, students, and staff. Excellent relational and communication skills are necessary. A desire to develop, implement, and benchmark new tools and methods is highly advantageous. Numerous opportunities for training to complement current skills are available at Columbia and through our collaborative network with other outstanding computational scientists. Travel to conferences is fully supported. Initial salary is commensurate with experience. Applying for independent funding is not required, but is strongly encouraged for the career development of the candidate.

  Relevant publications: PMIDs 31156022, 31112904, 29525040

  Research funded by NIA, NIMH, NIGMS