Bioinformatics Scientist - Virtual Cells

Prima MenteSan Francisco, CA
11d

About The Position

Prima Mente is a frontier biology AI lab. We generate our own data, build general purpose biological foundation models, and translate discoveries into research and clinical outcomes. Our first goal is to tackle the brain: to deeply understand it, protect it from neurological disease, and enhance it in health. Our team of AI researchers, experimentalists, clinicians, and operators is based in London, San Francisco and Dubai. Role focus - Virtual Cells Clearly articulate and interrogate meaningful biological hypotheses for marker and target discovery related to neurological diseases, via a virtual cell model.

Requirements

  • Top tier publications.
  • Patent experience.
  • First-hand experience with generating biological insights from biological data for virtual cells (genomics, epigenetics, transcriptomics, proteomics, ATAC-Seq).
  • Operational software engineering skills: the ability to write high-quality code to be the backbone of our software stack.
  • Experience with end-to-end tools used to process multi-omic data from raw data all the way to analytical outputs. Good working knowledge of workflow managers, including but not limited to NextFlow.
  • Expertise with statistical approaches applied to evaluating biological datasets.
  • Strong working knowledge with cloud computing environments, including but not limited to AWS.
  • Domain knowledge with advanced artificial intelligence approaches and experience applying them to a biological context.
  • Familiar with data wrangling, analysis, and visualisation libraries using Python (preferable), R or Julia.
  • Strong data engineering knowledge, including but not limited to experience with Spark, Hadoop, NoSQL.

Responsibilities

  • Set up and framework experimental hypotheses focussed on available datasets and models, with downstream evaluation criteria to assess performance.
  • Identify and curate public and proprietary datasets across different multi-omic, multi-modal types.
  • QC and data analysis.
  • Multi-omic bioinformatics pipeline development.
  • Feature engineering and dataset preparation for downstream machine learning as well as foundational AI training and application.
  • Work with experimental and machine learning teams to confirm and refine computational findings.
  • Develop and manage specific external research collaborations with academic and industrial partners specific to AD biology.
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