Client Individualized Neoantigen Specific Therapy (iNeST) team, developing fully personalized cancer therapies. Bioinformatics plays a critical role in clinical and research efforts in iNeST, and in other related cancer immunotherapy programs. You will work on a bioinformatics pipeline for neoantigen characterization, applying cutting-edge methods and algorithms to next generation sequencing data. You will be embedded in a bioinformatics team, composed of bioinformatics scientists and engineers, and will primarily work on development of a neoantigen analysis pipeline. You will also be collaborating with a larger team composed of wet-lab scientists, clinicians, AI scientists, and engineers.
Integrate new public and in-house methods into the neoantigen pipeline; for example,
HLA loss detection, MHC antigen presentation prediction using artificial intelligence, tumor heterogeneity, and other features.
Collaborate with bioinformatics engineers and scientists to develop and test new methods.
Redevelop and deploy the neoantigen pipeline as a containerized workflow in the cloud environment, to be eventually used in the clinic.
Unit and system testing, both for robustness and correctness. This effort will involve the development of tools to simulate specific types of tumor sequencing data.
Required skills and experience:
At least MS in Computer Science, Bioinformatics, Computational Biology,or similar field.
Fluency in R and Python.
Experience working with next generation sequencing data, including DNA and RNA sequencing.
Basic understanding of genetics.
Deployment on an HPC cluster.
Workflow development in Nextflow or Cromwell.
Containerization and deployment to AWS.
Experience with version control, build/test automation, and other standard software engineering practices.
Experience developing effective and systematic tests.
Desirable skills and experience:
PhD in Computational Biology, Bioinformatics, Cancer Genetics/Genomics, or similar field.
Experience applying deep learning and a basic understanding of the underlying models.
General understanding of statistics and data science.- provided by Dice