We are seeking an outstanding postdoctoral researcher who can advance statistics and machine learning methods for computational biology in gene therapies and other oligonucleotide-based modalities. A qualified candidate is expected to have a research background in biostatistics and machine learning and be able to develop new algorithms for practical applications. The familiarity with computational biology is a plus. The role will report to the Simulation and Modeling Sciences group. We offer a supportive and collaborative environment for cutting-edge interdisciplinary research.
Develop and apply statistical and machine learning algorithms for multi-omics high-throughput datasets.
Write well-documented code individually and collaboratively within a high-performance scientific computing environment.
Effectively communicate the advantages and caveats of developed algorithms to internal audiences with diverse backgrounds.
Collaborate with computational and experimental scientists located at multiple locations.
Publish articles in top peer-reviewed journals and deliver scientific and technical presentations at internal and external venues.
Please see the details at:
Ph.D. in statistics, biostatistics, computer sciences, or a related technical field.
Well-cited journal publications.
Programming experience in Python, R, or equivalent languages.
Strong oral and written communication skills to work in a team-based environment.
Research experience in the analysis of high-throughput next-generation sequencing data.
Advanced coursework in molecular biology.
Experience with a cloud-based computational environment such as a SLURM based cluster.
Familiarity with gene therapies and other oligonucleotide-based modalities.