Mount Sinai Careers
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Uri was born in Tel Aviv and grew up in Miami before studying Math and Biology during college at NYU. He entered the PhD program in the Math department at MIT, after which he joined George Church’s lab at Harvard Med to train in genomics technology development. Uri developed some of the first protocols and software for next-generation sequencing of the antibody repertoire. He also collaborated with Ben Larman to help develop the PhIP-seq assay for high- throughput antigen specificity. During graduate school, Uri cofounded Good Start Genetics, one of the first diagnostics companies to bring NGS to the clinic. After earning his PhD, Uri worked at Cloudera, which builds Hadoop infrastructure for big data. During this time, he became a core member of the ADAM team developing software for scaling genomics analyses to millions of samples. In 2016, Uri joined the genetics faculty at Mt Sinai.
You will be a founding member of the lab. Together with the PI, you will be responsible for defining your research plan and executing it. Research directions are flexible, but example areas of interest include developing software tools for large-scale functional genomics, commensal metagenomics and microbiome analyses, immune repertoire sequencing, statistical methods for new immune assays, and software for highly-multiplexed immunohistochemistry. The lab follows software engineering best practices (e.g., git, unit testing, semantic versioning) and uses modern technology stacks (e.g., public clouds, Hadoop). Because the lab is new, you will have the opportunity to help build out a new research lab and will also be instrumental in defining the lab culture. You can also assist in training/mentoring graduate students.
You should have a PhD or equivalent experience in a quantitative life sciences field (e.g., bioinformatics, genomics, computational biology, statistics, computer science) and experience in or a willingness to learn good software engineering practices. Software engineers with an interest in genomics/immunology would also be considered. You should be somewhat polyglot with respect to programming languages and data analysis tools. Experience in (or willingness to learn) some of the following is a plus:
• high-level language like Python or R
• systems language like Java, C++, or Scala
• cluster computing (e.g., LSF, Grid Engine)
• distributed computing (e.g., Hadoop, Spark)
• cloud computing (e.g., AWS, GCE)
• statistical analysis, machine learning, "data science"
• devops (testing, continuous integration)
• image processing, especially in the context of microscopy
• lab automation