LifeMine Therapeutics

Job Title: Computational Biologist

Job Number: 21282

Location: Cambridge, MA

Job Description

We are looking for a Computational Biology Scientist for our Cambridge site with deep expertise in genomic analysis. This candidate will implement novel and open-source computational methods to fuel LifeMine’s fungal genome mining engine. The candidate will work closely with other Genomics’ team members to improve methods of high-throughput sequencing data and big data analysis.

Responsibilities

  • Develop and implement novel algorithms for the discovery and prioritization of biosynthetic gene clusters
  • Perform comparative genomics analyses and interpret results
  • Proactively evaluate new technologies and methods, keep up with current literature, and incorporate new knowledge into the genome analysis pipelines
  • Collaborate with other scientists in the Genomics group and other groups at LifeMine
  • Summarize findings and present results to collaborators

Required Skills

  • Ph.D. in Genomics, Computational Biology, Bioinformatics or related field
  • Proficiency in at least one major scripting language (Python, Perl, R, or similar)
  • Experience in working with large datasets and databases
  • Experience in developing bioinformatic workflows and executing them on the cloud
  • Experience in processing and analyzing high-throughput sequencing data (genomic and transcriptomic)
  • Extensive knowledge of common bioinformatics tools especially those used in high-throughput sequencing, genome annotation, and taxonomic and phylogenetic analysis.
  • Excellent written and oral communication skills
  • A sense of adventure and excitement to be part of the development of a new drug discovery paradigm

Preferred Qualifications

  • 2+ years of relevant experience
  • Background in the biochemistry of secondary metabolite biosynthesis
  • Demonstrated success and creativity in the area of eukaryotic  genomics, evidenced by a strong record of scientific accomplishment
  • Knowledge of fungal molecular genetics
  • Experience with Machine Learning algorithms

 

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