Requirements

  • You have a PhD in Bioinformatics, Biostatistics, Computational Biology or similar field with a strong scientific publication record. Minimum of 0-3 (Associate Scientist) or 3-5 (Scientist) years of relevant post-doctoral or industry experience is required.
  • You have extensive experience with large scale high dimensional cytometry or single cell sequencing data and in-depth knowledge of analytical methods for analysis of cytometry and single cell sequencing and multi-omics data, including dimensionality reduction, clustering, statistical modeling, computational phenotyping, cell type annotation, meta analysis, data integration and machine learning.
  • You have the ability to develop, compare and deploy novel analysis methods to advance biomedical research, with a thorough understanding of the underlying statistical frameworks.
  • You have experience working with high dimensional data to support biomarker discovery and translation (e.g. single cell sequence assays, genotype or WGS data, proteomics data, etc.)
  • You have demonstrated competence in languages such as R or Python for bioinformatics analyses with extensive experience in developing algorithms into functional informatics workflows (e.g. R packages)
  • You enjoy using creative and novel informatic approaches and datasets to bring new insights to biological problems, and then working closely with biomarker scientists to develop those ideas experimentally.
  • You have experience in translational research is at least one of the following therapeutic areas of Ophthalmology, Metabolism, Neuroscience, Immunology and Infectious Diseases (OMNI) and Oncology.
  • You are able to present complex results, both verbally and in writing, to computational and non-computational audiences.
  • You can work successfully in cross-functional teams to contribute to technical development of informatics workflows to support drug and biomarker development.
  • Candidates with experience in immunology, multi-omics, cytometry informatics, multi-omic cytometry, bulk and single-cell RNA sequencing, gene signature deconvolution and analysis, biomarker analysis, genome sequencing, immune repertoire sequencing, general bioinformatics, and/or data algorithms including machine learning will be given preference. 
  • Experience with implementing supervised automated gating algorithms on cytometry data is a plus.

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