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Denali Therapeutics is dedicated to developing breakthrough therapies for neurodegenerative diseases through our deep commitment to degeneration biology and principles of translational medicine.
We are seeking to recruit a Computational Biologist to join our Discovery Genomics team to study the biology underlying neurodegeneration, with a particular focus on microglial biology and neuroinflammation. The successful candidate has broad expertise in the analysis of genomics-scale data, including bulk and single-cell transcriptomics, and will use them to deeply characterize cellular state and responses to therapeutic interventions in vitro and in vivo.
You will join a group that values diversity of thought, continuous learning, and explores impactful ways of applying genomics to target and biomarker discovery. You are a strong communicator and will become a valued collaborator on cross-functional teams featuring e.g. Biologists, Chemists, Biomarker Scientists, Protein Engineers and Data Scientists.
Thoughtful design and reproducible analysis of genomics experiments, including e.g. single-cell / single-nuclei and spatial transcriptomics data, and distilling key takeaways.
Identifying and applying best practices to genomics data collection, analysis & sharing, making datasets findable, accessible, interoperable and reusable (FAIR).
Communicating your findings to specialists and non-specialists on cross-functional teams, and the broader research community through presentations and publications.
A PhD in a relevant field, including — but not limited to — Computational Biology, Bioinformatics, Genomics, Biostatistics, etc. with 0 - 3 years post-doctoral or industry experience.
Driven by the desire to make a positive impact on human health through rigorous collaborative science.
Proven track record of analyzing high throughput genomics data as evidenced by high quality publications and/or conference talks in this area.
Understanding of statistical approaches to the analysis of high-dimensional data. Previous experience in single cell transcriptomics is a plus.
Extensive experience with a high-level data analysis language like R or Python. Knowledge of the Bioconductor software ecosystem and version control (e.g. GitHub) is a plus.